Performance Enhancement of a Wearable Wireless ECG device using efficient Signal Processing Techniques

In rural India, about 22.9% of death are due to heart diseases. Non-accessibility to efficient healthcare services in rural areas is one of the leading causes of this loss of life. Even though wearable devices are considered to be one of the efficient ways to provide better healthcare services, many doctors discourage the usage of these devices due to the noise and motion artifacts present in the signals acquired by these wearable devices. This research work mainly focuses on the performance enhancement of `AmritaSpandanm', a wearable wireless ECG device that will enable to provide real-time ECG signals even when the patient is involved in routine activities. For this, a context aware system is designed and developed to continuously collect the physical activity, classify the real-time signals using an innovative classifier algorithm and tag the ECG signal based on the classifier results. Using the results from the classifier algorithm, the motion artifacts in the ECG dataare removed using two methods, namely Adaptive Filtering and Wavelet Transform. The complete system has been implemented and tested on 35 individuals. The results obtained using wavelet transform shows 99 percentage of classification compared to the adaptive filtering method and therefore, wavelet transform is a better method to remove the motion artifacts. Hence the proposed system is capable to capture both the physical activity and ECG data of individuals and to provide an ECG signal free from noise and motion artifacts.