Accurate detection of fiducial points in a seismocardiogram (SCG) is a challenging research problem for its clinical application. In this paper, an automated method for detecting aortic valve opening (AO) instants using the dorsoventral component of SCG signal is proposed. This method does not require electrocardiogram (ECG) as a reference signal. After pre-processing the SCG, multiscale wavelet decomposition is carried out to get signal components in different wavelet subbands. The subbands having possible AO peaks are selected by a newly proposed dominant multiscale kurtosis (DMK) and dominant multiscale central frequency (DMCF) based criteria. The signal is reconstructed using selected subbands, and it is emphasized using the weights derived from proposed relative squared dominant multiscale kurtosis (RSDMK). The Shannon energy (SE) followed by autocorrelation coefficients are computed for systole envelope construction. Finally, AO peaks are detected by a Gaussian derivative filtering based scheme. The robustness of the proposed method is tested using clean and noisy SCG signals from CEBS database. Evaluation results show that the method can achieve an average sensitivity (Se) of 94%, prediction rate (+P) of 90% and detection accuracy (ACC) of 86% approximately over 4585 analyzed beats.

The phonocardiogram (PCG) signal indicates closing instants of atrio-ventricular and semilunar valves, and this information can also be extracted from two major profiles of a seismocardiographic (SCG) cycle. This paper presents a method to extract fundamental heart sounds from a SCG signal. The proposed method employs discrete wavelet transform (DWT) for signal decomposition, and subsequently, center of gravity (CoG) of power spectrum for each of the subbands is computed. Our method can optimally select wavelet subbands for any combination of sampling frequencies of the signal and number of decomposition levels. Based on proposed CoG criterion, the signal is reconstructed from a number of selected subbands, and instantaneous Hilbert envelope is constructed for localizing peaks of the signal. Finally, a simple decision rule is applied to annotate S1 and S2 sounds of the PCG signal. Experimental results show that the heart sound waves S1 and S2 can be well localized with the help of one SCG cycle without using a reference ECG cycle.

Non-contact human body vital parameter measurements are getting more attention and have been extensively studied in a short span of time. One of such techniques is the heart rate (HR) estimation based on video recording of human face known as remote photoplethysmogram (rPPG). Usually, the recorded video gets contaminated due to illumination variation of ambient light, motion artifacts, and other environmental factors. Thus, extracting a reliable rPPG signal is a challenging task. In this paper, a novel spatial-temporal filtering method is proposed that utilizes 2D variational mode decomposition (2D-VMD) along with azimuthally averaged power spectrum density (AAPSD) and multimode kurtosis to extract a reliable rPPG signal. The robustness of the proposed algorithm is tested and validated using our own database and the publicly available standard dataset. The obtained experimental results are compared with the reference PPG signal measurements. Also, the proposed method is compared with the well-established independent component analysis (ICA)-based method. The performance results show that the non-contact HR estimated by the proposed method dramatically reduces the error and it proves our method to be superior than the existing method.

In this paper, we present noise-robust photoplethysmographic (PPG) based biometric authentication method for wireless body area networks and m-health applications. The method consists of four steps: (i) preprocessing of PPG signals, (ii) systolic peak detection, (iii) ensemble averaged pulsatile waveform extraction and (iv) pulsatile waveform similarity matching using a normalized cross correlation (NCC) measure. The performance of the proposed method is tested and validated using different types of PPG signals taken from the standard PPG databases. For predefined threshold of 0.997, the NCC-based PPG biometric method achieves an average false rejection rate (FRR) of 0.32 and false acceptance rate (FAR) of 0.32. Performance evaluation results show that the proposed method achieves consistent authentication results as compared to the other methods under different kinds of artifacts and noise.

This paper presents a simple unified ensemble averaged heartbeat extraction framework for noise-robust ECG-based biometric authentication using heartbeat morphology. The proposed method consists of three major steps: preprocessing, ensemble averaged beat construction, and similarity matching. At the preprocessing stage, the signal blocking and mean removal operations are performed. The ensemble averaging stage includes the steps of: discrete cosine transform (DCT) based filter for simultaneous removal of BW and PLI noises, straightforward Gaussian derivative filter (GDF)-based R-peak detector, period normalization and peak-centering, and ensemble averaging computation. At the similarity matching stage, we study the performance of both time-domain and wavelet-domain distance metrics for finding the similarity between a test ensemble ECG beat template and an enrolled ECG beat template. The performance of the proposed framework is tested and validated using different types of ECG signals taken from the standard ECG databases. Results show that the ECG-biometric method using normalized cross correlation (NCC) metric provides consistent verification results as compared to the other four methods under different noisy conditions and sampling rates. For predefined threshold of 0.97, the NCC-based ECG-biometric method achieves an average false rejection rate (FRR) of 11.6% and false acceptance rate (FAR) of 5.8% for 3969 imposters and 19404 test segments from enrolled subjects, respectively.

Background: Septic patients who develop acute respiratory failure (ARF) requiring mechanical ventilation represent a heterogenous subgroup of critically ill patients with widely variable clinical characteristics. Identifying distinct phenotypes of these patients may reveal insights about the broader heterogeneity in the clinical course of sepsis, considering multi-organ dynamics. We aimed to derive novel phenotypes of sepsis-induced ARF using observational clinical data and investigate the generalizability of the derived phenotypes. Methods: We performed a multi-center retrospective study of ICU patients with sepsis who required mechanical ventilation for ≥ 24 h. Data from two different high-volume academic hospital centers were used, where all phenotypes were derived in MICU of Hospital-I (N = 3225). The derived phenotypes were validated in MICU of Hospital-II (N = 848), SICU of Hospital-I (N = 1112), and SICU of Hospital-II (N = 465). Clinical data from 24 h preceding intubation was used to derive distinct phenotypes using an explainable machine learning-based clustering model interpreted by clinical experts. Results: Four distinct ARF phenotypes were identified: A (severe multi-organ dysfunction (MOD) with a high likelihood of kidney injury and heart failure), B (severe hypoxemic respiratory failure [median P/F = 123]), C (mild hypoxia [median P/F = 240]), and D (severe MOD with a high likelihood of hepatic injury, coagulopathy, and lactic acidosis). Patients in each phenotype showed differences in clinical course and mortality rates despite similarities in demographics and admission co-morbidities. The phenotypes were reproduced in external validation utilizing the MICU of Hospital-II and SICUs from Hospital-I and -II. Kaplan–Meier analysis showed significant difference in 28-day mortality across the phenotypes (p < 0.01) and consistent across MICU and SICU of both Hospital-I and -II. The phenotypes demonstrated differences in treatment effects associated with high positive end-expiratory pressure (PEEP) strategy. Conclusion: The phenotypes demonstrated unique patterns of organ injury and differences in clinical outcomes, which may help inform future research and clinical trial design for tailored management strategies.