Showing papers by "Samarendra Dandapat published in 2023"

Congestive Heart Failure Detection From ECG Signals Using Deep Residual Neural Network

Abstract: The early and accurate detection of congestive heart failure (CHF) using an electrocardiogram (ECG) is of great significance for improving the survival rate of patients. Existing approaches show limited detection accuracy as they fail to capture the temporal ECG dynamics. Also, these methods lack model transparency and are often difficult to interpret. This article proposes a novel end-to-end diagnostic attention-based deep residual recurrent neural network (DA-DRRNet) that effectively captures the temporal dynamics and extracts high-level attentive representations for accurate CHF detection. Specifically, we first employ a recurrent neural network (RNN) layer to encode the temporal dynamics from the raw ECG beats. Then, multilayered RNNs with residual connections are incorporated to extract high-level feature representations hierarchically. The residual connections allow gradients in deep RNN to propagate effectively, thereby improving the network’s representation ability. Finally, an attention module identifies the hidden vectors corresponding to the diagnostically prominent ECG characteristics to form an attentive representation for improved CHF detection. Using ECG signals from the three publicly available datasets (BIDMC-CHF, PTBDB, and MIT-BIH NSRDB), the proposed method achieves an impressive accuracy of 98.57% and nearly 100% for beat-level and 24-h record-level diagnosis, respectively. Notably, the analysis of learned attention weights demonstrates that the proposed model focuses on the clinically relevant ECG features that characterize CHF. This model transparency and improved detection results advance research in this field and provide a reliable and transparent diagnostic system for CHF analysis.

Automated Detection of Heart Valve Diseases Using Stationary Wavelet Transform and Attention-Based Hierarchical LSTM Network

Abstract: This article presents a phonocardiogram (PCG)-based heart valve disease (HVD) detection for primary healthcare units. In this work, we propose a stationary wavelet transform (SWT) decomposition, followed by an attention-based hierarchical long short-term memory (HLSTM) network for each subband to detect HVDs. Initially, the PCG signal is preprocessed and segmented into PCG segments. Then, each PCG segment is decomposed using SWT. We employ subband-specific HLSTM networks to utilize the temporal and relative temporal information present in each subband of the PCG segment for detecting HVDs. Then, the outputs of each subband-specific HLSTM are fed to their respective intra-subband attention layer for weighted aggregation to obtain the subband-specific representation. Furthermore, the inter-subband attention layer aggregates these subband-specific representation vectors to improve the detection of HVDs. The proposed method is tested and validated using two open-access databases. The Physionet Challenge 2016 database shows an overall sensitivity (OSe) of 97.96%, overall specificity (OSp) of 99.02%, and overall accuracy (OA) of 98.55%, using the proposed method for binary classification. Similarly, the heart sound (HS) murmur database shows impressive classwise performance measures and an OA of 99.47%, using the proposed method for multiclass HVD classification. The proposed method’s impressive performance and generalization can help to detect HVD anomalies during preliminary checkups in healthcare units.

Stationary Wavelet Transform Based Detection of Aortic Stenosis Using Seismocardiogram Signal

Abstract: Aortic stenosis (AS) is one of the most common and severe valvular heart diseases, which can cause heart failure. Early detection and treatment are the most effective ways to prevent AS. This study proposes a stationary wavelet transform (SWT) based machine learning framework to detect AS using seismocardiogram (SCG) signal. First, the SCG signal is preprocessed and segmented into cardiac cycles. Then, SWT is deployed to decompose each cardiac cycle into subbands. Further, each subband is used to extract the novel statistical features, which include log-energy entropy, relative wavelet energy, multi-scale kurtosis, and median absolute deviation. Finally, these features are fed into the random forest classifier for automated detection of AS. The effectiveness of the proposed method is evaluated using data from two publicly available databases. Our method achieves an overall accuracy of 99.4%, sensitivity of 98.9%, and specificity of 99.9%. The proposed method provides comparable performance with the current state-of-the-art techniques. The impressive results of the proposed framework make it useful to detect AS in primary healthcare units.