RESEARCH WORK
What I’ve Done
A LOW-COST, LOW-ENERGY WEARABLE ECG SYSTEM WITH CLOUD-BASED ARRHYTHMIA DETECTION
Continuously monitoring the Electrocardiogram (ECG) is an essential tool for Cardiovascular Disease (CVD) patients. In low-resource countries, the hospitals and health centers do not have adequate ECG systems, and this unavailability exacerbates the patients’ health condition. Lack of skilled physicians, limited availability of continuous ECG monitoring devices, and their high prices, all lead to a higher CVD burden in the developing countries. To address these challenges, we present a low-cost, low-power, and wireless ECG monitoring system with deep learning-based automatic arrhythmia detection. Flexible fabric-based design and the wearable nature of the device enhances the patient’s comfort while facilitating continuous monitoring. An AD8232 chip is used for the ECG Analog FrontEnd (AFE) with two 450 mi-Ah Li-ion batteries for powering the device. The acquired ECG signal can be transmitted to a smartdevice over Bluetooth and subsequently sent to a cloud server for analysis. A 1-D Convolutional Neural Network (CNN) based deep learning model is developed that provides an accuracy of 94.03% in classifying abnormal cardiac rhythm on the MIT-BIH Arrhythmia Database.
A LIGHTWEIGHT CNN MODEL FOR DETECTING RESPIRATORY DISEASES FROM LUNG AUSCULTATION SOUNDS USING EMD-CWT-BASED HYBRID SCALOGRAM
Listening to lung sounds through auscultation is vital in examining the respiratory system for abnormalities. Automated analysis of lung auscultation sounds can be beneficial to the health systems in low-resource settings where there is a lack of skilled physicians. In this work, we propose a lightweight convolutional neural network (CNN) architecture to classify respiratory diseases using hybrid scalogram-based features of lung sounds. The hybrid scalogram features utilize the empirical mode decomposition (EMD) and continuous wavelet transform (CWT). The proposed scheme's performance is studied using a patient independent train-validation set from the publicly available ICBHI 2017 lung sound dataset. Employing the proposed framework, weighted accuracy scores of 99.20% for ternary chronic classification and 99.05% for six-class pathological classification are achieved, which outperform well-known and much larger VGG16 in terms of accuracy by 0.52% and 1.77% respectively. The proposed CNN model also outperforms other contemporary lightweight models while being computationally comparable.
CARDIOXNET: A NOVEL LIGHTWEIGHT CRNN FRAMEWORK FOR CLASSIFYING CARDIOVASCULAR DISEASES FROM PHONOCARDIOGRAM RECORDINGS
The alarmingly high mortality rate and increasing global prevalence of cardiovascular diseases (CVDs) signify the crucial need for early detection schemes. Phonocardiogram(PCG) signals has been historically applied in this domain owing to its simplicity and cost-effectiveness. However, insufficiency of expert physicians and human subjectivity affect the applicability of this technique, especially in the low-resource settings. For resolving this issue, in this paper, we introduce CardioXNet,a novel lightweight CRNN architecture for automatic detection of five classes of cardiac auscultation namely normal, aortic stenosis, mitral stenosis, mitral regurgitation and mitral valve prolapse using raw PCG signal. The process has been automated by the involvement of two learning phases namely, representation learning and sequence residual learning. The first phase mainly focuses on automated feature extraction and it has been implemented in a modular way with three parallel CNN pathways i.e., frequency feature extractor (FFE), pattern extractor (PE) and adaptive feature extractor (AFE). 1D-CNN based FFE and PE respectively learn the coarse and fine-grained features from the PCG while AFE explores the salient features from variable receptive fields involving 2D-CNN based squeezeexpansion. Thus, in the representation learning phase, the network extracts efficient time-invariant features and converges with great rapidity. In the sequential residual learning phase,because of the bidirectional-LSTMs and the skip connection, the network can proficiently extract temporal features. The obtained results demonstrate that the proposed end-to-end architecture yields outstanding performance in all the evaluation metrics compared to the previous state-of-the-art methods with up to 99.6% accuracy, 99.6% precision, 99.6% recall and 99.4% F1-score on an average while being computationally comparable.