Premature ventricular contraction (PVC) is the type of ectopic heartbeat, commonly found in the healthy population and is often considered benign. However, they are reported to adversely affect the accuracy of R-R variability based electrocardiographic (ECG) algorithms. This study proposes a Principal Component Analysis (PCA) based algorithmic approach to detect the PVCs based on their morphology. The eigenvectors were derived from signal window around the R-peak, where signal window for the PVC (wPVC) and that of NSR (wNSR) were set to 0.55 seconds and 0.16 seconds respectively. We used 24 ECG recordings from MIT BIH arrhythmia database as training dataset and the remaining 24 ECG recordings as testing dataset. Using the derived eigenvectors and the Linear regression (LR) analysis; complexes corresponding to the wNSR and wPVC were estimated from training and testing datasets. Four different classification methods were employed to differentiate between wPVS and wNSR, namely, Root mean squared error (RMSE), Pearson product-moment correlation coefficient comparision, Histogram probability distribution and k-Nearest Neighbour (KNN). All four methods were implemented individually to classify the wPVC and wNSR. The performance of each of the classification approach was evaluated by computing sensitivity and specificity. With the sensitivity of 93.45% and specificity of 93.14%, KNN based classification method has shown the best performance. The method proposed in this study allows for an effective differentiation between NSR beats and PVC beats.
Heart rate variability analysis (HRVA) gives valuable insight to the cardiovascular system. Electrocardiogram (ECG) based HRVA has been assessment gold standard but eavesdropping of wearable technology requires the comparison of its surrogacy to an accepted standard. In this study, optical and mechanical measures at distal artery waveform are compared to the electrical signal of the heart. The sensor data of the six healthy volunteers are collated and compared at fiducial points in various time, frequency and non-linear domains for HRVA. We have found that during early systole fiducial location on waveforms can be surrogate to ECG standard and mechanical sensor 2 nd derivative proved to be the best among them. Also, the comparative technology shows enormous potential for cardiovascular diagnostic.
In the last four decades, hypertension doubled to 1.13 billion patients. High blood pressure (BP) is the main risk factor for cardiovascular morbidity and mortality. Arterial stiffness (AS) is a key component and poorly understood part of cardiac vital signs. Pressure-volume loop (PU-Loop) has been used to measure local pulse wave velocity (PWV) which is an indicator of AS [1]. We have been able to measure the PU-Loop noninvasively on palmar digital arteries. Pressure and flow waveforms are measured simultaneously at the same location. The dataset has calculated the normalized PWV of 1.48±0.4 from the slope of the line formed between two early systole points of 20-30%. PU-Loop provides an insight into contractility, preload, and hypertension and correcting factor for pulse transit time estimations.
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