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According to World Health Organization (WHO) cardiovascular diseases (CVDs) are the number one cause of global deaths annually. More than 17 million people die each year from CVDs. To diagnose CVDs some clinical tests are required which are invasive and non-invasive both and are also expensive. Hence, there is a need for cheap, reliable non-invasive sensing that is easily available and can be used by layman. We are presenting a method that can harvest energy from touchpad for bio sensing. Today, almost everyone has a smartphone and its capacitive touchscreen can be used as a probable sensor for sensing vital health parameters. This paper presents the feasibility of developing a heart rate monitoring system using capacitive touch sensing. Tests were performed on six participants falling in different age groups. Scaled data collection could not be done because of coronavirus outbreak. Each participant underwent tests under normal heart rate and fast heart rate conditions. The experimental data obtained was tested in the frequency and time domain. Results suggest that the absolute error percentage in estimating the mean heart rate (beats.min-1) in the frequency domain is less than 5% for almost all the participants. The accuracy percentage for some participants in measuring the mean heart rate in beats per minute is 100%. The average mean absolute error (MAE) in the time domain is reported to be 3.83 beats. min-1 for participants enduring normal heart rate condition. Participants persisting with fast heart rate condition reports to an average MAE of 8.59 beats. min-1.

In this paper, we propose a cardiovascular digital twin platform to simulate the effect of exercise on various cardiac parameters of medical importance. The model incorporates the real-time ECG signal from the body-worn sensors to estimate exercise level and compute cardiac variables like left ventricular dynamics, cardiac output, ejection fraction, mean arterial pressure, etc., of an individual while performing exercises. The novel contribution of this work is to determine the cardiac compliances from the morphology of the single-lead ECG signal and Systemic resistance estimation from the information of the exercise level for a specific subject performing physical activities. Simulation results are produced employing an open-source database (Troika), and it shows medically accepted trends in all the derived cardiac variables. The proposed framework can aid to progressively track the cardiovascular efficiency during exercise for patients with cardiac co-morbidity and act as a personalized therapy guide in a care continuum scenario.

Worldwide revenue of pharmaceutical market is more than 1200 billion USD [1] and that of counterfeit medicines is around 200 billion USD [2][3]. Counterfeit medicines can be detected by technical experts using visual inspection or through sophisticated lab and relevant methods. However, such methods require time, sample preparation and technical expertise with lab setup. These methods are not feasible and scalable to be used in the field by the general public. The objective of our research work was to detect counterfeit medicines using simpler and faster method using hyperspectral sensing. In this experiment, a visible – near infrared (350nm – 1050nm) hyperspectral device was used to capture spectral signature of the medicines. We used 24 medicine tablets of different companies. To imitate counterfeit medicines, tablet powders were adulterated by adding different levels of calcium carbonate. Spectral signatures were captured from original stage to all stages of adulterations and analyzed using machine learning (multilayer perceptron classifier). Result shows that we are able to achieve more than 90% classification accuracy. Portable hyperspectral sensing combined with medicines spectral database can be a good field level test method for detection of counterfeit medicines, as it is very fast, easy to use and does not require technical expertise.

Sepsis is one of the most prevalent causes of mortality in Intensive Care Units (ICUs) and also one of the most expensive health-care problems. Delayed treatment is associated with increase in death and financial burden. This work proposes an early prediction of sepsis validated on Physionet Challenge 2019 dataset. The challenge is to extract continuous, categorical and domain-specific discriminating features from highly sporadic lab data and vital signals. We find that the imputation of extremely isolated data lower the prediction performance. In order to mitigate this, we use a sliding window on sporadic data to generate continuous features which capture the trend. We also devise a binning approach to generate categorical features from the aperiodic data in order to discriminate the deviation from normalcy. Lastly, we observe that a logical fusion of Random Forest and Logit Boost provides optimal performance. Normalized Utility Score (NUS) is used to benchmark the performance of the proposed baselines. Five-fold cross-validation of the best preforming pipeline across the data reveals high median NUS of 0.401.

Objective Atrial fibrillation (AF) and other types of abnormal heart rhythm are related to multiple fatal cardiovascular diseases that affect the quality of human life. Hence the development of an automated robust method that can reliably detect AF, in addition to other non-sinus and sinus rhythms, would be a valuable addition to medicine. The present study focuses on developing an algorithm for the classification of short, single-lead electrocardiogram (ECG) recordings into normal, AF, other abnormal rhythms and noisy classes. Approach The proposed classification framework presents a two-layer, three-node architecture comprising binary classifiers. PQRST markers are detected on each ECG recording, followed by noise removal using a spectrogram power based novel adaptive thresholding scheme. Next, a feature pool comprising time, frequency, morphological and statistical domain ECG features is extracted for the classification task. At each node of the classification framework, suitable feature subsets, identified through feature ranking and dimension reduction, are selected for use. Adaptive boosting is selected as the classifier for the present case. The training data comprises 8528 ECG recordings provided under the PhysioNet 2017 Challenge. F1 scores averaged across the three non-noisy classes are taken as the performance metric. Main result The final five-fold cross-validation score achieved by the proposed framework on the training data has high accuracy with low variance (0.8254 [Formula: see text] 0.0043). Significance Further, the proposed algorithm has achieved joint first place in the PhysioNet/Computing in Cardiology Challenge 2017 with a score of 0.83 computed on a hidden test dataset.

Valvular heart diseases are a prevalent cause of cardiovascular morbidity and mortality worldwide, affecting a wide spectrum of the population. In-silico modeling of the cardiovascular system has recently gained recognition as a useful tool in cardiovascular research and clinical applications. Here, we present an in-silico cardiac computational model to analyze the effect and severity of valvular disease on general hemodynamic parameters. We propose a multimodal and multiscale cardiovascular model to simulate and understand the progression of valvular disease associated with the mitral valve. The developed model integrates cardiac electrophysiology with hemodynamic modeling, thus giving a broader and holistic understanding of the effect of disease progression on various parameters like ejection fraction, cardiac output, blood pressure, etc., to assess the severity of mitral valve disorders, namingMitral StenosisandMitral Regurgitation. The model mimics an adult cardiovascular system, comprising a four-chambered heart with systemic, pulmonic circulation. The simulation of the model output comprises regulated pressure, volume, and flow for each heart chamber, valve dynamics, and Photoplethysmogram signal for normal physiological as well as pathological conditions due to mitral valve disorders. The generated physiological parameters are in agreement with published data. Additionally, we have related the simulated left atrium and ventricle dimensions, with the enlargement and hypertrophy in the cardiac chambers of patients with mitral valve disorders, using their Electrocardiogram available in Physionet PTBI dataset. The model also helps to create ‘what if’ scenarios and relevant analysis to study the effect in different hemodynamic parameters for stress or exercise like conditions.

Cardiopulmonary disease prognosis can achieve therapeutic edge if the disorders can be detected and attended to at an early stage. This work proposes 'Cardiopulmonary Care Platform (C2P)' which in its current initial phase, targets at subjects in the stage of Functional Capacity II as per the NYHA staging system by detecting physiological fatigue, and signs of dispnea and palpitation, using a smartwatch after the subject has undergone a spell of physically intensive activity. A novel and computationally efficient solution is devised to reconstruct breathing cycles from raw PPG signal recorded from the smartwatch, which enables the system to continuously monitor the subject's heart rate and breathing pattern online. The method achieves high accuracy when verified against a precalibrated flow-meter. Also a novel and an accurate up/down stairs detection algorithm using onboard atmospheric pressure sensor is discussed. We also present a study of physiological measurements like heart rate, breathing rate and breathing signal power as possible markers for fatigue.

In this paper, we present a methodology for classifying normal, atrial fibrillation (AF), non-AF related other abnormal heart rhythms and noisy recordings by analysing single lead ECG signal of short duration. In a two layer binary cascaded approach proposed in our methodology, an unlabelled recording is initially classified into one of the two intermediate classes (‘normal+others’ and ‘AF+noisy’) at the first layer before actual classification at the second layer The Physionet Challenge 2017 dataset containing more than 8500 ECG recordings are used for creation of training models and interval validation. The proposed methodology yields an average F1-score of 0.91, 0.79 and 0.77 respectively in classifying normal, AF and other rhythms on the training dataset using 5-fold cross validation. Results also show that, the said methodology, when applied on a hidden test set maintained by the challenge organisers yields F1-score values of 0.92, 0.86 and 0.74 in classifying the same.