IIT researchers develop device that can predict risk of CVD

India: In a major development, the researchers from the Indian Institute of Technology (IIT) Hyderabad have designed a device that can facilitate early diagnosis, therapy, and prognosis of cardiovascular disease (CVD). The low-power device can monitor electrocardiogram (ECG) and alert patients and doctors in real-time about the risk of CVD and prevent mortalities.

A team of researchers, consisting of Vemishetty Naresh and Amit Acharya of the IIT-Hyderabad designed the device to diagnose medical conditions from ECG data on a real-time basis. The research is published in the peer-reviewed international journal Scientific Reports.

According to WHO, CVDs are the number 1 cause of death globally, taking an estimated 17.9 million lives each year. CVDs are a group of disorders of the heart and blood vessels and include coronary heart disease, cerebrovascular disease, rheumatic heart disease, and other conditions. Four out of 5 CVD deaths are due to heart attacks and strokes.

Individuals at risk of CVD may demonstrate raised blood pressure, glucose, and lipids as well as overweight and obesity. These can all be easily measured in primary care facilities. Identifying those at the highest risk of CVDs and ensuring they receive appropriate treatment can prevent premature deaths.

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As a proof of concept demonstration, the researchers have taken healthy and various unhealthy cases from the Physionet database to validate the proposed method. Besides, they also worked on developing different classification techniques and integrating them to make a generic algorithm. A System-on-Chip (SoC) architecture is developed in a low complex way by resource sharing concept for the CVD automation. Thus, the whole system can cover various ECG abnormalities and finally come up with the prototype board which looks similar to a smartphone.

"CVD is one of the deadliest diseases and irrespective of the economy of the country people are getting affected by it. It is manifested in different forms necessitating early diagnosis, therapy, and prognosis. Hence the proposed work on the classification is going to be of immense help for the society, said Dr Acharyya, speaking on the importance of this research.

Speaking about their plans to take this research to benefit the society at large, Mr Naresh said, "There is an exponential increment in human mortality rate, due to the delayed diagnosis, lack of proper distribution of health care facilities and prognosis centres in the vicinity. There is a need for a robust automated device for the early detection of the vital abnormal ECG signals in chronic CVD patients.

This medical science and technological needs impose many challenges on such device development such as low power consuming system design tradeoff between the on-board processing and RF (Radio Frequency) communication, low complexity analogue front-end circuit design and energy harvesting or self-power mechanism to prolong battery life.

Further, there is a great necessity to develop a robust algorithm to find any desynchronization in the ECG waves. With the present advancement in technology, there is a great scope for developing robust medical ECG devices in analyzing the ECG signals and classify the patient's condition. This method will predict the departure from the healthy condition to an unhealthy condition corresponding to the CVDs.

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Working Description

The researchers proposed a novel System-on-Chip (SoC) architecture for CVD monitoring. Scrutinizing the technical challenges like low-power and low-area for delivering reliable healthcare under resource constraints, they have proposed low-complex Boundary Detection (BD) and Feature Extraction (FE), low-complex f-QRS Detection and Morphology Identification (FDMI) architecture, Rule Engine (RE), and Token-based compression technique. Researchers aim to take this idea further to the system level from the concept and propose a low-complexity but medically reliable SoC architecture.

The above-proposed methodologies are used to extract the essential clinical features from each ECG beat and compared with the standard values to give the binary classification as normal or abnormal. Many research articles had put thrust on studying the clinical features of ECG beats and the heart rate variability for the diagnosis. Despite these findings, it is difficult to derive an equivocal temporal relationship of these methodologies to predict arrhythmias. The prediction of ECG abnormalities associated with the change of morphology in the localized features (PR interval, QRS complex, QT interval) will allow the clinicians sufficient time to intervene to stop its escalation causing sudden cardiac death. To mitigate the above limitations our attempt in this thesis is to propose a generalized Phase Space Reconstruction (PSR) based detection and classification of the CVD by exploiting the localized features of the ECG, unlike the state-of-art PSR techniques.

The study, "Phase Space Reconstruction Based CVD Classifier Using Localized Features," is published in the journal Scientific Reports.

DOI: https://doi.org/10.1038/s41598-019-51061-8