Epilepsy Archives - InnoHEALTH magazine

Classification of Inter-Ictal Epileptic Seizure using combined Machine Learning and Deep Learning Approach

InnoHEALTH magazine digital team — Fri, 24 Jun 2022 08:45:25 +0000

Epilepsy is the most rarely recognizable neurological disorder characterized by an enduring predisposition to exaggerate recurrent seizures and that fatally affects the individual.

Prediction of Epileptic seizure is recently blooming as the most challenging task in order to amend the life of a patient. Specially, the inter-ictal state of Epilepsy needs more diagnostics attention for its unpredictable interrupted properties. In order to analyze the EEG recordings, various machine learning techniques have been implemented but many of them lack in bringing the brain network analysis into account which is the most vital way to predict, diagnose and detect the neural disorder with its level best accuracy. In this paper, EEG signals are collected from an open source and preprocessed by using Discrete Wavelet Transform where the features are extracted. In the next step, the features are transformed into a robust multi-dimensional array retaining its spatial properties. In the succeeding step, the array sequence is fed to a Deep Convolutional Neural Network to classify the disease using the training data.

A Detailed Description on Epileptic Seizure

Epilepsy is the most rarely recognizable neurological disorder characterized by an enduring predisposition to exaggerate recurrent seizures and that fatally affects the individual. Any abnormal neural activity localized in the cerebral cortex is called epileptic seizure (ES). The seizure causes the normal brain network to evoke neurons in a self-sustained hyper-synchronized manner and ultimately affects cognitive function. According to a survey of World Health Organization (WHO), 70 million people worldwide are suffering from epilepsy trails, however recognized as most rarely detected brain dysfunction. The task of detecting or predicting ES is still a concerned research since the past three decades. Inter-ictal ES emerges from random spikes, slow yet sharp complex neuro waves which is different from clinically observed ES and mostly the symptoms are observed in children. However, the automated detection and prediction algorithms depending on electroencephalographic (EEG) measurements are characterized for the transition of signals from the inter-ictal to the ictal state by identifying the image patterns significantly. Therefore, the baseline inter-ictal properties are vital. However, many inherent assumptions are commonly implicated to monitor the EEG activity during this inter-ictal state which is relatively constant and interrupted during seizure occurrence.

To determine the type of seizure and brain areas involved, an Electroencephalogram (EEG) is performed. EEG has various unparalleled properties for its immense usages to study ES such as signals are recorded with high temporal resolution and low cost, and systems are capable of both long term and portable monitoring. Capitalizing on the specific properties of EEG, a number of EEG based approaches have been developed for the automatic prediction of epileptic activity. The analysis of EEG signals for the purpose of ES detection and prediction have been advanced with the help of the most efficient machine learning technique such as Brain Network analysis. Networks in a regular, lattice-like configuration are characterized by high clustering and a long average path length. In recent longitudinal studies, we are more concerned about graph-based brain network analysis, in which the nodes in the graph are represented by the electrodes while the links are defined by the measure of association between the nodes. Accordingly, we found increases in average clustering and path length and decreased weight dispersion indicating that normal brain maturation is characterized by a shift from random to more organized small-world functional networks. However, this analysis is receded for the reason that it required the entire graphical data to be processed simultaneously which is less effective for the graphs with billions of nodes and edges.

To address the above challenges, the present investigation has sought to develop a novel prediction strategy for seizure detection based on Neural Network (NN) analysis during inter-ictal state of seizure.

To address the above challenges, the present investigation has sought to develop a novel prediction strategy for seizure detection based on Neural Network (NN) analysis during inter-ictal state of seizure. We have considered the ES data, remove the random noises from the data by using Discrete Wavelet Transform (DWT) and then convert them into more robust multi-dimensional array tensors and obtain a sequence whose topology retains spatial information. Once all the frameworks of sequences are gathered, they are fed into D-CNN for classification. ES is predicted by inter- and intra- individual generalized properties.

1. Preprocessing of Data

The dataset being used in this paper are imported from CHB-MIT open source contains scalp EEG data of 23 patients recording 844 hours of seizure occurrence facing total 163 seizures.

Fig. 1 Data captured using 22 electrodes at sampling rate of 256 Hz.

Fig. 2 Tree structure of DWT

Fig. 3 Framework of the proposed system

In Fig. 2 the tree structure of DWT is shown where the original image is decomposed in time domain using a high pass filter (HPF) and a low pass filter (LPF) sequentially. Later on, it is down sampled by 2 to calculate each level using the coefficient values.

This experiment follows two major steps; primarily data processing and followed by a classifier. In data processing, the original images obtained from patients are extracted using DWT. In the second step, the preprocessed data is fed to D-CNN for the prediction of epilepsy.

The proposed technique

Fig. 4(a) 2-DCNN Model

The architecture of D-CNN was predominantly developed in 80’s. The technique is updated and refined periodically and becomes the most improved deep learning method during the 21st century. The latest version of D-CNN is unwrapped as compared to earlier known neural networks. The trending technique occupies a multi-layered architecture well compatible in the domain of digital image processing, computer interfaced medical imaging and medical image analysis. It commands significantly with high resolution spatial image approachable for prediction, classification and segmentation problems. The block of D-CNN has multiple Convolutional layers, pooling layers and one fully connected layer as shown in figure 4 (a) and (b) depicting two different layered CNN models such as 2-DCNN and 4-DCNN respectively.

Architecture of 2-DCNN model is the combination of two similar types of DCNN models comprises of a kernel size (3×3) with 32 filters each. The pooling layer has a pool size of (2×2). Its activation function is known as softmax activation function.

The 4-DCNN model is the combination done by a simple concatenation of the two similar 2-DCNN models with softmax as activation function.

To validate the efficacy of the classifier, four types of desire classes are distinguished such as True Positive (TP), False Positive (FP), True Negative (TN) and False Negative (FN).

Fig. 4(b) 4-DCNN Model

Fig. 5 Confusion Matrix to classify the seizure

Performance index of the classifier is validated by considering the values of confusion matrix.

TP: These are the accurately predicted positive values interpreting the actual class to be true and the predicted class to be true.
TN: These are the accurately predicted negative values interpreting the actual class to be false and the predicted class to be false.
FP: When the actual class is false and the predicted class is true.
FN: When the actual class is true but the predicted class is false.

Further analysis on the matrix is performed by the following formulas such as:

In the future, predicting onset seizures can be further improved by reducing the training time and thereby, allowing doctors to diagnose the disease more quickly in an organized manner.

In the future, predicting onset seizures can be further improved by reducing the training time and thereby, allowing doctors to diagnose the disease more quickly in an organized manner. Therefore, future research should be conducted to reduce the number of parameters available in the model. This research work needs to be extended by adding Electromyogram (EMG) Electrocardiogram (ECG) data, implicating simplified feature extraction techniques and improving the number of supervised and unsupervised classifiers.

Composed by: “Prateek Pratyasha is presently pursuing Ph.D. under the department of Biomedical Engineering at National Institute of Technology Raipur. Her areas of research are cognitive recognition, neural plasticity, artificial intelligence and Optogenetics.”

“Dr. Saurabh Gupta is an Assistant Professor of Biomedical Engineering, at National Institute of Technology Raipur. His primary areas of research are inverse problems, medical imaging and stochastic optimization, to develop technologies for community medicine and public health. “

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Indian Hemp: Neglected Pharmacological Treasure Trove

InnoHEALTH Magazine — Wed, 13 Nov 2019 08:22:51 +0000

Hemp or Cannabis sativa with limited concentration of an active psychotropic substance tetrahydrocannabinol (THC) <0.3% is a promising industrial crop. It is known for its use in paper industry, textile, biodegradable plastics, paint, biofuel, food and feed. Historical evidence of its use can be traced in ancient literature Atharvaveda (1500 BC); Sushruta Samhita for treatment of phlegm and diarrhea and Persian medical text of 18th century (UnaniTibbi). Cannabis sativa, due to its close relative Cannabis indica (high THC Cannabis), always remained under shadow. It can be a useful source of non-psychotropic cannabidiol (CBD). The recent advances in CBD research and identification of Cannabinoid receptors in human revolutionized its importance in western pharmacology. The use of flowers/buds for cannabidiol (CBD), a non-addictive substance, is extremely useful for pharmacological formulation managing chronic pain, anxiety, inflammation, depression etc.

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The processing of flower and buds of Cannabis is illegal in India. Flower and bud trichome’s release psychotropic substances formulated as charas, hashish and ganja. Phytocannabinoids accumulate in the secretory cavity of the glandular trichomes, which largely occur in female flowers and in most aerial parts of the plants. The seeds and leaves are permitted for usage depending on state policy though licensing. Ironically, since ages peoplein India have been consuming bhang milkshake (a preparation of C. sativa or Vijaya) as sacred drink during Lord Shiva related rituals, though its cultivation was legally banned in 1985 by Narcotic Drugs and Psychotropic Substances Act. This act came after International Treaty of 1961 regarding Narcotics Substances.

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Mechoulam (2005) described hemp as “neglected pharmacological treasure trove”. Cannabinoids are now recognized more because of their relation with human endocannabinoid system which includes two G proteincoupled cannabinoid receptors (CB1 and CB2) and two ligands (anandamide and 2-arachidonylglycerol). They can modulate variety of physiological responses such as pain, memory, inflammation, appetite, behavior etc. In contrast to Cannabinoids, THC is partial agonist of above mentioned receptors with higher binding with CB1. This may be associated with its known psychoactive abilities. Even though THC’s are more potent as a drug which can be used for various treatment protocols, the associated side effects makes it inferior candidate of choice in contrast to CBD. CBD can also reduce THC negative effects and help in improving efficacy of Cannabis based products. CBD is very promising cannabinoid and has been successfully used in preclinical studies of nervous system related diseases (epilepsy, schizophrenia etc.). However, at present, itsuse in therapeutic treatments is still limited.

In India, some States have been issued a license to produce hemp either in collaboration with Industrial Association, CSIR based pharmaceutical initiatives or to fulfill the requirement of R&D based activity under AYUSH regulations. Hemp is considered as a source of biomass, fiber, and high-value oil. Fibers based textile market is legal in India, however, at present, it is facing fierce competition. The availability of cheaper yarn from Bangladesh (full exemption of basic customs duty) and China’s favor to Vietnam (by waiving import duty) is further fueling the competition. Indian hemp based textile industry is still in nascent stages. Despite high technological advancement available in India, the use of Hemp in providing CBD moieties for new global pharmacological formulations is still not a reality. Even our ecosystem is not ready due to the non-availability of legal backing; technological innovations for using Hemp based fabric for

new quality material; lack of standardization of hemp cultivation with targeted % of CBD as being done in other parts of the world and also state-wise variation in the implementation of the law.

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Industrial uses of Hemp provide significant business opportunity, for example some companies have developed Eco-friendly bricks made out of hemp. These bricks absorb carbon monoxide and are durable as cement bricks. The production of hemp bricks can provide economic benefit venture for Indian farmers. This brick-based material was used in preserving Ellora Caves for 1500 years. In India, the major problem in any such venture is ‘middlemen policy’ which makes end product expensive with least benefit to the farmer (licensing cost etc.). Permission for contract farming lacks legal support. India, unlike developed countries, neither has strict regulation nor it is relaxed enough to cater as uniform business opportunity. Furthermore ‘stigma’ attached to hemp cultivation (confusion with marijuana) and lack of industrial linkage to farming units with direct export potential further reduces its industrial potential.

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On the other hand, without proper monitoring and uniform policy, relaxation of laws might aggravate the situation at ground level. As per understanding of Ayurvedic perspective, the plant resin (especially from wild plants at 2000 or 3000 m height) is charas which is an extremely powerful banned narcotic drug. However, its leaf based formulation bhang is used in Ayurvedic preparation as stimulant, aphrodisiac and also as sedative. The use of CBD in Indian Ayurvedic Formulations (with traditionally known 191 Ayurvedic preparations for 29 different diseases), regulated and approved by AYUSH after clinical testing, are still in a nascent stage. AYUSH medicines still need to bridge gap between their standards with globally accepted norms. The legal aspects of excise duties on medicinal or toilet preparation containing Indian Hemp limits its probable utility. It means when business is exploring its potential crossing excise department, agriculture department, narcotics department, there is no single window approach feasible. This environment-friendly, pest resistant and sustainable crop is self sustainable after few production cycles and can even be easily regulated by the provision of raw material i.e. seeds only via government sources.

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Keeping the vast market potential of hemp in both medical and food industry and significant pressure faced by Indian farmers, major attention from government sources is required. Firstly, amendments to existing regulations to tap complete potential of this industrial crop (especially regulated and controlled contract farming) is necessary. Secondly, the implementation of law requires adequate State machinery with upgradation of Indian Industrial Hemp Association to a forum for awareness, training and feedback platform. The integration of Central Digital Policy for linking of Aadhaar system to transfer benefits directly to farmers by controlling middlemen. Lastly, the development of uniform code throughout the country addressing excise, narcotics, medicinal, research and agriculture/export policy related to Indian Hemp multiple industrial uses with special focus on promising pharmacological relevance.

About the author

Dr. Sarita Jaiswal, Chief Scientific Officer at Carpere, University of Saskatchewan, Canada, is an accomplished Plat Scientist having 15+ years of R&D experience with specialization in cereal and pulse crop biochemistry and genomics. She has been awarded twice for the category of Young Scientist (Indian Society of Plant Physiology and amp; KK Nanda Foundation for Advancement of Plant Sciences).

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Epilepsy is the Fourth Most Common Neurological Disorder

InnoHEALTH Magazine — Wed, 23 Oct 2019 11:04:03 +0000

Artificial Intelligence can help decode epileptic brains. Epilepsy is the fourth most common neurological disorder affecting nearly 65 million people worldwide. The seizures or ‘fits’ as is commonly known, arise due to unusual electrical activity in the brain and is the chief symptom of epilepsy. Neither dependent on age or gender, the onset of the seizure is unpredictable without a set pattern of frequency of occurrence or severity, often posing a challenge to the caregiver.

Did you know that one student commits suicide every hour in India?

Although epilepsy can be related to previous brain injuries or genetic factors, neurologists have found unprovoked, recurrent seizures in healthy individuals too. How and why these seizures occur remains a mystery. However, research has found that the source of seizures is within the brain. In other words, the brain itself is the generator of epilepsy.

Spatial maps of top 10 networks: If the origin is within the brain, then are there any fingerprints that can be detected? Does the brain often tell-tale signs which can be mapped to predict the tendency of epilepsy?

Reason for Aggression After Drink

Seeking answers to these questions, a team of interdisciplinary researchers conducted a study to peep inside epileptic brains. The results indicate that there exist independent neural networks that can carry disease sensitive information about the anomaly. With the help of machine learning models and artificial intelligence, researchers were able to detect and reveal the hidden patterns.

“Epilepsy is not a disorder but the manifesting of something from within the brain’s electrical activity. Interestingly, each one of us has the neural map of epilepsy within our brain. It is only when the network gets fired and manifests externally, in a recurrent manner, it becomes disorder or epilepsy,” explained Dr. Tapan Kumar Gandhi, lead researcher of the study from Indian Institute of TechnologyDelhi, while speaking to India Science Wire.

The usual diagnosing tool for epilepsy is by EEG (Electroencephalography) readings of epileptic patterns and visible symptoms like convulsions, loss of consciousness or sensory disturbances.

Existing studies reveal specific patterns that represent synchronous activities of sensory, auditory, cognitive and other functions. These activities are indicated by the change in blood flow to the brain and seen as BOLD signals or changes in the Blood-Oxygen-Level-Dependent output.

Recent developments in Magnetic Resonance Imaging or MRI help picture these activities in the brain and detect the cause of seizures such as a lesion or scar. However, MRI is not very useful when a seizure flares up. Whereas, functional MRI — another scanning method — can record regional interactions in the brain when a particular task is being performed.

In 1995, Indian researchers had found that the brain shows prominent neural network connections even in its resting state. Termed as resting-state functional MRI or rsfMRI, the images from this scanning indicate neural patterns in an individual’s brain even when no action is performed.

In the present study, the team utilized rsfMRI technique and performed brain scans on individuals with Temporal Lobe Epilepsy (TLE), which is the most common form of epilepsy.

Dr. Gandhi said, “We hypothesized that there could be ‘disease-specific networks’ in epilepsy prone brain that can be identified with the help of the machine learning model.” Machine learning involves artificial intelligence to read live data instead of pre-programmed information. Such a building block of a machine is analogous to a neuron cell in the brain.

Researchers used a tool called Support Vector Machine (SVM) to deal with the complex and non-linear data obtained from the scans. By using another algorithm called Elastic-net based ranking, the relevant features of the neuroimaging data were extracted. The signals were integrated to reveal the patterns.

The team conducted a pilot study on 132 subjects – 42 with epilepsy, and the rest with healthy individuals. Parameters like age, gender, history of epilepsy, genetic predisposition, incidents of injuries, medications and more, were taken into account. The epilepsy patients underwent three rsfMRI while those in the healthy group were scanned once.

In all, 88 independent components or networks were obtained from the whole brain imaging data and fed as input to the SVM. From the patterns, top 10 strong networks were correlated with clinical features using another standard method called Pearson’s Correlation to generate the rsfMRI epileptic neural networks.

From the pattern inputs, the SVM could identify epileptic individuals to an accuracy of 97.5% and specific lobes in the brain responsible for the condition. The model also revealed correlations such as the age of onset, frequency of seizures, or duration of illness.

By this, researchers concluded that the independently derived rsfMRI contains epilepsy-related networks. ‘Our research establishes that with the help of machine learning methods, we can identify these networks, as we had hypothesized. Increased strength in these networks indicates the possibility of a progressing Temporal Lobe Epilepsy’, explained Dr.Gandhi.

The team included Rose Dawn Bharath, Sujas Bharadwaj, Sanjib Sinha, Kenchaiah Raghavendra, Ravindranadh C. Mundlamuri, Arivazhagan Arimappamagan, Malla Bhaskara Rao, Jamuna Rajeshwaran, Kandavel Thennarasu and Parthasarathy Satishchandra (National Institute of Mental Health and Neurosciences, Bengaluru); Tapan K. Gandhi and Jeetu Raj (IIT, Delhi); Rajanikant Panda (Universitè de Liège, Belgium); Ganne Chaitanya (Thomas Jefferson University, USA) and Kaushik K. Majumdar (Indian Statistical Institute, Bengaluru). The study results have been published in the journal European Radiology.

Credits: India Science Wire

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Study : Long Term Impact of Stroke

InnoHEALTH Magazine — Mon, 26 Nov 2018 09:36:42 +0000

A new study looks at what problems affect people most after a stroke and it provides a broader picture than what some may usually expect to see.

Stroke affects more than just physical functioning, according to a study recently published in Neurology, the medical journal of the American Academy of Neurology.

“People who have an only mild disability can often have ‘hidden’ problems that can really affect their quality of life,” said study author Irene L. Katzan, MD, MS, of the Cleveland Clinic in Ohio and a member of the American Academy of Neurology.

“And for people with more disability, what bothers them the most? Problems with sleep? Depression? Fatigue? Not many studies have asked people how they feel about these problems, and we doctors have often focused just on physical disability or whether they have another stroke.”

The study involved 1,195 people who had an ischemic stroke or a stroke where blood flow to part of the brain is blocked. They were asked questions about their physical functioning, fatigue, anxiety, sleep problems, thinking skills such as planning and organizing, how much their pain affects other aspects of their life and their satisfaction with their current social roles and activities. Participants took the questionnaires an average of 100 days after their stroke, and about a quarter of the participants needed help from a family member to fill out the questionnaires. Researchers also measured their level of disability.

The people with stroke had scores that were considerably worse than those in the general population in every area except sleep and depression. Not surprisingly, the area where the people with stroke were most affected was physical functioning, where 63 percent had scores considered meaningfully worse than those of the general population, with an average score of 59, where a score of 50 is considered the population average.

On the question about whether they were satisfied with their social roles and activities, 58 percent of people with stroke had scores meaningfully worse than those of the general population.

“People may benefit from social support programs and previous studies have shown a benefit from efforts to improve the social participation of people with stroke, especially exercise programs,” said Katzan.

The thinking skills of people with stroke in executive functions, or planning and organizing, were also affected, with 46 percent having scores that were meaningfully worse than the population average.

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“The social participation and executive functioning skills are areas that have not received a lot of attention in stroke rehabilitation,” Katzan said. “We need to better understand how these areas affect people’s well-being and determine strategies to help optimize their functioning.”

Limitations of the study include that the questionnaires did not ask about other problems that can occur after stroke, such as communication issues.

Also, the study participants had milder strokes on average than people with stroke overall and the average age of participants was 62, which is lower than the average age of 69 for people with stroke overall.

Informatively, American Academy of Neurology is the world’s largest association of neurologists and neuroscience professionals, with 34,000 members. AAN is dedicated to promoting the highest quality patient-centered neurologic care. A neurologist is a doctor with specialized training in diagnosing, treating and managing disorders of the brain and nervous system such as Alzheimer’s disease, stroke, migraine, multiple sclerosis, concussion, Parkinson’s disease, and epilepsy.

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Rural healthcare: Solutions lie in innovations

InnoHEALTH Magazine — Mon, 23 Apr 2018 11:10:53 +0000

It has become imperative to make rural and remote areas healthy and free of diseases and infections to make ‘National Health Mission’ a success. There are still many parts of the country where medical services are still not accessible. With the iKure’shealthcare delivery system, the dream of providing minimum health service to the rural parts of the country has become true. Armed with Wireless Health Incident Monitoring Systems, Community Health Workers have been at forefront to provide doorstep solutions at minimal cost. iKure’shealthcare has become a new frontier to drive innovations in rural healthcare delivery

There are over 330 CHWs who are trained and strategically deployed across six states in rural India and have been rendering services under iKure’shealthcare delivery system.

Jayashree is one the CHW who works relentlessly in her village Hubli(Karnataka). One day, she visited a woman named Pushpa who was in her first trimester and looked quite unwell.On enquiring, Pushpa was found to suffer from Epilepsy. Her family also revealed that she has been prescribed with medicines by a local doctor, but she didn’t take them regularly.

Epilepsy is a disorder of the nervous system and such patients during pregnancy have major concerns. Jayashree felt the need and promptly captured her vitals on her smartphone loaded with Wireless Health Incident Monitoring Systems, (WHIMS), and pushed the patient’s vitals on to the cloud for a quick doctor reference.

For the next few days, Jayashree monitored and screened her closely and counselled her and family about different preventive measures pertaining to her health conditions. Soon after, Pushpa recovered and gave birth to a healthy child.

In the context of rural settings, it is never possible to create doctors or health clinics as required. To bridge the gap, the grassroots community members are empowered to provide clinical careenabled through WHIMS.The technology application is designed with an intuitive graphics user interface that can be efficiently used by a health worker even with a basic education.

For instance, WHIMS screen has a complete photograph of a human body.When a health worker clicks on the head, WHIMS gives options of potential symptoms of the head, such as ache, dizziness, seeing dark spots, and so on.

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Usually with the standard diagnostic tool, low accessibility and portability remain a huge constrain. WHIMS is accompanied with low-cost portable instruments to measure basic statistics and loaded on a tablet is carried by the CHWs in the remotest villages to diagnose patients. The patients pay an affordable rate for basic diagnostics and consultations, through which they earn additional income.

Unlike the Accredited Social Health Activists (ASHA workers) who work as community mobilizer and not authorized to deliver clinical services, these CHWs meet both the ends.

Equipped with a medical kit that consists of devices including BP machine, stethoscope, pulse oximeter, thermometer, first aid kit, weight machine, Height measuring scale and gloves,they carry medicines for basic common ailments, non-invasive hemoglobin meter, haemoglobin kit, mobile phone based sphygmomanometer, urine analyzer, calorimeter, ECG, flipcharts/ Hoardings & registers.

Through these CHWs, every patient is also provisioned with Digital Health Card (DTH) encrypted with a QR (Quick Response) code and during home visits. DTH gets updated with latest clinical indicators. The health data obtained from the health cards is synced to a cloud for effective utilization in the upward heath chain at both secondary and tertiary levels.

CHW’s contact and familiarity within the community drive effective community mobilization measures. As they become an integral part of the healthcare delivery model, CHWs act as a catalyst to drive behavioral change. While they interacts and communicates the necessity on the ground and identifies appropriate health needs, it is through street plays, hoardings and message dissemination on mobile phones, theymobilize patients at different levels. They are also attuned to provide nutritional counselling, and spectacle services based on the community needs.

Further, WHIMS is upgraded with robust supply chain management framework, innovative algorithm, and Data Warehousing solutions. Driven by its CHWs, iKure aims to bring efficiency in public system & programe and improve lives.

The CHW model of empowering the women community and gender mainstreaming represents transformational change in rural healthcare delivery. As they champion tech-savvy cultural values in otherwise austere facilities, and limited supply of doctors, such effort has established inspirational leaders driving catalyst change in sustainable rural development.

“For a basic diagnostic long distance travel time, high cost of services and prolonged waiting hours at the local PHCs was a huge burden for these rural residents. Use of P-O-C devices is extremely portable, affordable with high accuracy rate and can be used by any health worker with a short training. This has been a critical step in rural healthcare delivery with a positive outcome on prevention, curative care and treatment procedures”.

Dr. Ajay Agrawal, Medical
Director-iKure

“Initially when I used to visit pregnant women, they were apprehensive and reserved. But repeated visits have given them confidence to open up. They ask many questions, show doctor’s report and ask for medicines. I have a joint family and few among them are pregnant, even they are educated, but they would consult me and ask for my opinion. I feel valued and found a voice through iKure”.

Gayathri Acharya
iKure CHW, Hubli, Karnataka

(Author: Dr. Tirumala Santra Mandal is a research and communication analyst at iKure. With a doctorate degree in corporate communication from IIT Kharagpur, she has worked in diverse fields of communications such as lie detection, multimedia communication, communicative English. Prior to working with iKure, she has worked as an editor and authored several publications in referred and business journals both at national and international levels.)

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Centre for Childhood Neuro developmental Disorders at AIIMS

InnoHEALTH Magazine — Mon, 26 Mar 2018 11:10:13 +0000

The genetic tests would help in antenatal counselling preventing further birth of affected children in families and unveiling of newer pathogenic and therapeutic pathways.

The All India Institute of Medical Sciences will soon establish the Centre for Advanced Research and Excellence for Childhood Neuro developmental Disorders.

The Centre of Excellence will come up at the Mother and Child Block at an approved amount of Rs 14.3 crore. The broad category of Neuro developmental disorders to be covered by the CoE would be Neuromotor impairments, Motor, language and cognitive delay and epilepsy. Vision and hearing impairments will be covered as comorbidities.

According to AIIMS Deputy Director Administration V. Srinivas, the proposal for establishing the CoE was conceptualised by a team of senior faculty led by Prof V. K. Paul and Prof Sheffali Gulati.

The state-of-the-art Centre will provide high quality affordable care, perform high end diagnostic and therapeutic research and act as an education and training hub.

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The five key elements of the CoEare –Creation of a National Registry, National Knowledge and Training Centre, Advanced Diagnostics, Advanced Therapeutics& Rehabilitation and a helpline for Tele-consultation.

The National Registry will be the first of its kind in the country paving the way for meaningful research, policy making and advocacy for childhood neuro development disorders.

A standardised list of investigations for diagnosis of neuro developmental disorders would be developed including advanced genetic investigations in collaboration with Institute of Genomics and Integrative Biology. The genetic tests would help in antenatal counselling preventing further birth of affected children in families and unveiling of newer pathogenic and therapeutic pathways.

The Centre will focus on use of technology-aided rehabilitation strategies for children with neuro developmental disorders. This will be done in collaboration with IIT Delhi utilising textile technology and biomedical engineering. It will provide reliable and round the clock toll-free telephonic guidance.

The India Infrastructure Finance Company Limited as part of its CSR activities has collaborated for the purpose.

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