Jayanta Saha

Bio: Jayanta Saha is an academic researcher from Medical College and Hospital, Kolkata. The author has contributed to research in topic(s): Beat detection & Lossy compression. The author has an hindex of 2, co-authored 5 publication(s) receiving 14 citation(s).

Preserving Abnormal Beat Morphology in Long-Term ECG Recording: An Efficient Hybrid Compression Approach

Abstract: In long-term electrocardiogram (ECG) recording for arrhythmia monitoring, using a uniform compression strategy throughout the entire data to achieve high compression efficiency may result in unacceptable distortion of abnormal beats. The presented work addressed a solution to this problem, rarely discussed in published research. A support vector machine (SVM)-based binary classifier was implemented to identify the abnormal beats, achieving a classifier sensitivity (SE) and negative predictive value (NPV) of 99.89% and 0.003%, respectively with 34 records from MIT-BIH Arrhythmia database (mitdb). A hybrid lossy compression technique was implemented to ensure on-demand quality, either in terms of distortion or compression ratio (CR) of ECG data. A wavelet-based compression for the abnormal beats was implemented, while the consecutive normal beats were compressed in groups using a hybrid encoder, employing a combination of wavelet and principal component analysis. Finally, a neural network-based intelligent model was used, which was offline tuned by a particle swarm optimization (PSO) technique, to allocate optimal quantization level of transform domain coefficients generated from the hybrid encoder. The proposed technique was evaluated with four types of morphology tags, “A,” “F,” “L,” and “V,” from mitdb database, achieving less than 2% PRDN and less than 1% in two diagnostic distortion measures for abnormal beats. Overall, an average CR of 19.78 and PRDN of 3.34% was obtained. A useful outcome of the proposed technique is the low reconstruction time in rapid screening of long arrhythmia records, while only abnormal beats are presented for evaluation.

Compression of Multilead Electrocardiogram Using Principal Component Analysis and Machine Learning Approach

Abstract: In this work, a multi-lead Electrocardiogram (ECG) data compression using principal component analysis (PCA) combined with a machine learning technique is proposed to achieve a high compression ratio (CR) with low reconstruction error (within 2% percentage root mean squared difference, or, PRD). The beat detection procedure was inspired by the Pan-Tompkins algorithm with some necessary modifications. A lead-wise PCA decomposition was performed for dimensionality reduction with a single beat from each lead at a time using a fixed energy reconstruction criteria. The optimal quantization levels of the principal components were allocated using multi-layer perceptron neural network (MLP-NN) using lead clinical features as the input. This MLP-NN was tuned offline by a particle swarm optimization (PSO) generated data for quantization level of coefficients of PC as the reference. The proposed technique was evaluated using 8 types of cardiac abnormalities record from multi-lead ECG data from the PTB Diagnostic ECG database, with an average CR, PRD and PRDN of 16.2, 1.47% and 1.84% respectively. The reconstructed records were clinically acceptable. The proposed technique provides superior performance than few recent published works on multilead ECG compression.

Congenital Pulmonary Vein Stenosis and Pulmonary Artery Branch Stenosis: A Rare Combination

Abstract: Congenital pulmonary vein stenosis is a rare entity caused due to failed incorporation of common right and/or left pulmonary vein into the left atrium. Below is a case report of a combination of predominantly left-sided pulmonary vein stenosis with right pulmonary artery branch stenosis. The patient was an adolescent boy with mild symptoms. Clinical examination revealed features of pulmonary artery hypertension. Echocardiography and computed tomography scan were done to confirm the disease.

Two case reports of retrieval of peripherally inserted central catheter from pulmonary artery and dislodged stent from arteria profunda femoris using improvised snares

Abstract: As the number of catheterization laboratory procedures increase, there is more incidence of retention of foreign body in the form of torn catheters or devices or stents. Improvised snares are necessary in the absence of readymade snares. Retrieval of peripherally inserted central catheter from the pulmonary artery can be very challenging sometimes. Very few case reports are available regarding retrieval of dislodged stent from right profunda femoris artery.

A Comparative Evaluation of Ischemic Mitral Regurgitation in AMI Patients Managed by Revascularization Vs Optimal Medical Therapy

Abstract: Objectives: Ischemic mitral regurgitation (IMR) is one of the frequent complications associated with coronary artery disease (CAD); but the optimal management of IMR is controversial. Our aim was to evaluate and compare the impact of medical therapy versus revascularization on the degree of MR.Methods: We performed observational follow up study on 114 patients admitted to our hospital with AMI and mild to moderate degree of MR. Multiple parameters were used to assess the severity of MR at baseline and after 1 year of follow up to assess the change in MR severity after medial therapy and revascularization.Results: In the medically managed group, MR grade improved in 28.57% of patients while 53.57% patients remained in the same grade as before. The grade of MR deteriorated from moderate to severe in 17.86% patients during follow up. In revascularization group; improvement in MR grade was observed in 60.71% of patients while 32.14% patients remained in the previous grade. Deterioration from moderate to severe occurred in 7.14% of patients. PCI and CABG subgroup analysis showed almost similar impact on degree of MR during follow up.Conclusion: From our study we concluded that revascularization; either in form of PCI or CABG, is associated with improvement in degree of MR when compared to optimal medical therapy alone.

Computed Tomography Angiography and Magnetic Resonance Angiography of Congenital Anomalies of Pulmonary Veins.

Abstract: We aimed to review computed tomography and magnetic resonance angiography of congenital anomalies of pulmonary veins. Total anomalous pulmonary venous return shows all pulmonary veins drain abnormally in another site rather than left atrium. Imaging can detect anomalous veins either supracardiac, infracardiac, or mixed. Partial anomalous pulmonary venous return shows some pulmonary vein have abnormal drainage that well delineated with computed tomography angiography. Scimitar syndrome is a type of partial anomalous pulmonary venous return where the pulmonary veins of the right lung drain infracardiac and is associated with right lung hypoplasia and dextrocardia. Pseudoscimitar show anomalous vein that takes a tortuous course and drains into the left atrium producing a false-positive scimitar sign. Cor triatriatum shows septum divide left atrium with proximal chamber receives blood flow from the pulmonary veins. Levoatriocardinal vein is an anomalous connection between the left atrium and anomalous vein from systemic venous system that is embryo logically derived from the cardinal veins. Computed tomography angiography can detect pulmonary vein stenosis, atresia, hypoplasia, and varix. Imaging is important for intimal diagnosis and detects the anomalous vessels and its connection, presence of stenosis, and associated other congenital cardiac anomalies. Also, it is a great role in assessment of patients after surgery.

A Dynamic Approach for Compressed Sensing of Multi–lead ECG Signals

Abstract: This paper proposes a dynamic method based on Compressed Sensing (CS) to reconstruct multi-lead electrocardiography (ECG) signals in support of Internet-of-Medical-Things. Specifically, the sensing matrix is dynamically evaluated through the signal samples acquired by the first lead. The experimental evaluation demonstrates that, compared to the traditional CS multi-lead method adopting a random sensing matrix, the proposed dynamic method exhibits a lower difference from the original ECG signal.

A new approach of ECG steganography and prediction using deep learning

Abstract: In this paper, a new approach of ECG steganography of hiding patient’s confidential information is proposed. As steganography results in distortion within the ECG signal which hampers the clinical features, in this work, encryption was performed within TP-segment of ECG. Additionally, segment classification and feature extraction were used for data concealing within normal TP-segments, while keeping abnormal segments unaffected. To reduce the computational complexity and execution time, encryption was performed in time domain signal, using a new approach. Finally, after decryption of hidden data, to predict original sample values of modified TP-segments, a long short-term memory recurrent neural network (LSTM-RNN) was used which efficiently reduced the error between the original and predicted signal. This algorithm was successfully implemented on mitdb, ptbdb and European ST-T database, available in physionet and percent root mean square difference (PRD), PRD normalized (PRDN) were obtained less than 1% along with signal to noise ratio (SNR) and peak SNR (PSNR) more than 80 dB. It was observed that this algorithm provided better result among other frequency domain techniques and recently published works.

Complex study on compression of ECG signals using novel single-cycle fractal-based algorithm and SPIHT.

Abstract: Compression of ECG signal is essential especially in the area of signal transmission in telemedicine. There exist many compression algorithms which are described in various details, tested on various datasets and their performance is expressed by different ways. There is a lack of standardization in this area. This study points out these drawbacks and presents new compression algorithm which is properly described, tested and objectively compared with other authors. This study serves as an example how the standardization should look like. Single-cycle fractal-based (SCyF) compression algorithm is introduced and tested on 4 different databases-CSE database, MIT-BIH arrhythmia database, High-frequency signal and Brno University of Technology ECG quality database (BUT QDB). SCyF algorithm is always compared with well-known algorithm based on wavelet transform and set partitioning in hierarchical trees in terms of efficiency (2 methods) and quality/distortion of the signal after compression (12 methods). Detail analysis of the results is provided. The results of SCyF compression algorithm reach up to avL = 0.4460 bps and PRDN = 2.8236%.

Quality Aware Compression of Multilead Electrocardiogram Signal using 2-mode Tucker Decomposition and Steganography

Abstract: In this article, a quality controlled compression of multilead electrocardiogram (MECG) is proposed, based on tensor analysis, and implemented upon 3D beat tensor of MECG. To reduce computational complications and execution time, a new approach of principal component analysis (PCA), based on 2-mode Tucker Decomposition, is introduced. In order to maintain relevant features of MECG after reconstruction, multi agent supervised learning system (MASLS) based optimal quantization of each fiber of core tensor is introduced, to limit the percentage root mean squared difference (PRD) within a specified value, maintaining high compression ratio (CR). The MASLS is previously trained offline, using features of tensor fibers, along with optimized quantization levels of those fibers, obtained from a particle swarm optimization (PSO), as reference. In addition, to hide patient’s confidential information, steganography is performed within the core tensor followed by generation of a’ secret key’, which is necessary, while decrypting those information during reconstruction. The whole algorithm is implemented on several MECG records, available in PTB Diagnostic ECG database, and compression result is compared by formation of n-beat tensor separately, using ‘n’ number of successive (‘n’ = 5, 10 and 15) beats. After testing on 547 data, average CR of 22, 41.5, 55.4, PRD of 3.62, 4.96, 5.59 and PRD normalized (PRDN) of 3.61, 4.94, 5.57 are achieved for 5, 10, 15-beat tensor, respectively. This proposed algorithm has provided superior result as compared to recently published works on MECG data compression.