Jayanta Saha

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

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.

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.

An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image

Abstract: Polycystic ovary syndrome (PCOS) is the most prevalent endocrinological abnormality and one of the primary causes of anovulatory infertility in women globally. The detection of multiple cysts using ovary ultrasonograpgy (USG) scans is one of the most reliable approach for making an accurate diagnosis of PCOS and creating an appropriate treatment plan to heal the patients with this syndrome. Instead of depending on error-prone manual identification, an intelligent computer-aided cyst detection system can be a viable approach. Therefore, in this research, an extended machine learning classification technique for PCOS prediction has been proposed, trained and tested over 594 ovary USG images; where the Convolutional Neural Network (CNN) incorporating different state-of-the-art techniques and transfer learning has been employed for feature extraction from the images; and then stacking ensemble machine learning technique using conventional models as base learners and bagging or boosting ensemble model as meta-learner have been used on that reduced feature set to classify between PCOS and non-PCOS ovaries. The proposed technique significantly enhances the accuracy while also reducing training execution time comparing with the other existing ML based techniques. Again, following the proposed extended technique, the best performing results are obtained by incorporating the "VGGNet16" pre-trained model with CNN architecture as feature extractor and then stacking ensemble model with the meta-learner being "XGBoost" model as image classifier with an accuracy of 99.89% for classification.