SAMIUL BASED SHUVO

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.

THE EFFECTS OF ANATOMICAL POSITIONS ON TRANSDERMAL INSULIN DELIVERY MODEL USING MICRONEEDLE PATCH

Transdermal delivery is a recent phenomenon in terms of smart drug delivery. Diabetes has been one of the leading causes of death throughout the world. Besides death, diabetic patients need to take regular doses of insulin for maintaining normoglycemic conditions. Transdermal patches can be limited in their efficacy for delivering insulin as it has a high molecular weight. Personalization of insulin delivery patches can result in optimal drug delivery, varying anatomical position, and controlling insulin uptake within the body. The objective of this work was to quantify the change in insulin concentration while using a transdermal insulin patch with multiple microneedles along with convectional blood flow. To this end, COMSOL has been used for insulin concentration simulation at multiple locations of the body and at different skin positions. It has been found, shoulder and forearm are the suitable locations for bolus insulin delivery while the abdomen is better suited for basal insulin delivery. The simulation showed a 66% difference in insulin concentration at blood flow between shoulder and abdomen location. This work can improve our understanding of insulin patch placement and thus transdermal therapy. Here, the validated model can work as a great starting block for further simulation including glucose control simulations.
… Read more

A ROBUST DEEP LEARNING FRAMEWORK FOR REAL-TIME DENOISING OF HEART SOUND

The heart sound signal captured via a digital stethoscope is often corrupted with environmental and physiological noise, altering its salient and critical properties. The problem is exacerbated in crowded low-resource hospital settings with high noise levels. In this study, inspired by the heart sound’s quasi-periodic nature, we present a novel deep encoder-decoderbased LU-Net architecture to suppress ambient hospital and internal lung sound noises that corrupt the heart sounds. Training is done using a large benchmark PCG dataset mixed with physiological noise, i.e., breathing sounds. Two different noisy datasets were created for experimental evaluation by mixing unseen lung sounds and hospital ambiance noises with the clean heart sound recordings. We also use the inherently noisy portion of the PASCAL heart sound dataset for evaluation. Experimental results show that the proposed framework can effectively suppress background noise in both real-world and synthetically generated noisy heart sound recordings, improving the signal-to-noise ratio (SNR) level by 5.575 dB on an average using only 1.32 M parameters. The study reveals that the proposed model outperforms the current state-of-the-art U-Net model with an average SNR improvement of 5.613 dB and 5.537 dB in the presence of lung sound and hospital noise, respectively. LU-Net also outperformed the state-of-the-art Fully Convolutional Network (FCN) by 1.750 dB and 1.748 dB for lung sound and hospital noise conditions, respectively. In addition, the proposed denoising method model improves classification accuracy by 38.93% in the noisy portion of the PASCAL heart sound dataset. The proposed enhancement scheme can thus play a vital role in deploying automated cardiac screening systems in low-resource and underserved communities.