Abstract: The present invention relates to a method and system for early detection of COVID-19. Existing methods require data from multiple sensors for training a prediction model whose output is considered as final prediction which is actually the prediction for a particular day or time instance. However, this prediction doesn't detect actual infection of COVID-19 since it requires monitoring the change in health of the user over consecutive days. Embodiments of present disclosure overcome these challenges by a prediction model for COVID-19 which requires only data from Photoplethysmography (PPG) sensor seamlessly collected from a wearable device still able to provide accurate COVID-19 prediction with application of a post processing technique on the predictions of the prediction model. Since COVID-19 symptoms have an effect on heartrate and oxygen saturation which are effectively captured by PPG sensor data, studying these dynamics during infection period gives insights to perform early detection of COVID-19.

Abstract: A system and method for estimating blood pressure (BP) using photoplethysmogram (PPG) has been explained. The PPG is captured from a PPG sensor (102). For preparing training model, a pulse oximeter is used for capturing PPG. For testing, a smartphone camera is used for capturing the PPG signal. A plurality of features are extracted from the preprocessed PPG signal. A BP distribution is then generated using the plurality of features and the training model. The BP distribution is part of a set of BP distributions generated from different subjects. Finally, a post-processing methodology have been used to reject inconsistent data out of the set of BP distributions and BP value is estimated only for the remaining BP distributions and a statistical average is provided as the blood pressure estimate.

Abstract: A method and system for blood pressure (BP) estimation of a person is provided. The system is estimating pulse transit time (PTT) using the ECG signal and PPG signal of the person. A plurality of features are extracted from the PPG. The plurality of PPG features and the PTT are provided as inputs to an automated feature selection algorithm. This algorithm selects a set of features suitable for BP estimation. The selected features are fed to a classifier to classify the database into low/normal BP range and a high BP range. The correctly classified normal BP data are then used to create a regression model to predict BP from the selected features. The current methodology uses automated feature selection mechanism and also employs a block to reject extreme BP data. Thus the available accuracy in predicting BP is expected to be more than the existing BP estimation methods.

Abstract: A method and system for removing corruption in photoplethysmogram (PPG) signals for monitoring cardiac health of patients is provided. The method is performed by extracting photoplethysmogram signals from the patient, detecting and eliminating corruption caused by larger and transient disturbances in the extracted photoplethysmogram signals, segmenting photoplethysmogram signals post detection and elimination of corruption caused by larger and transient disturbances, identifying of inconsistent segments from the segmented photoplethysmogram signals, detecting anomalies from the identified inconsistent segments of the photoplethysmogram signals, analysing the detected anomalies of the photoplethysmogram signals and identifying photoplethysmogram signal segments corrupted by smaller and prolonged disturbances.

Abstract: A system and method for estimating blood pressure (BP) using photoplethysmogram (PPG) has been explained. The PPG is captured from a PPG sensor (102). For preparing training model, a pulse oximeter is used for capturing PPG. For testing, a smartphone camera is used for capturing the PPG signal. A plurality of features are extracted from the preprocessed PPG signal. A BP distribution is then generated using the plurality of features and the training model. The BP distribution is part of a set of BP distributions generated from different subjects.

Abstract: A method and system of detecting arrhythmia using photoplethysmogram (PPG) signal is provided. The method is performed by extracting photoplethysmogram (PPG) signals from a patient, extracting cardiac parameter from the extracted photoplethysmogram (PPG) signals, identifying presence of cardiac abnormalities as reinforcement filtering of detecting premature ventricular contraction and ventricular flutter from the extracted cardiac parameters, analysing the extracted cardiac parameters to investigate statistical trend and to perform statistical closeness approximation of the extracted photoplethysmogram (PPG) signals and predicting and subsequently classifying type of arrhythmia.

Abstract: This disclosure relates generally to biomedical signal processing, and more particularly to method and system for physiological parameter derivation from pulsating signals with reduced error. In this method, pulsating signals are extracted, spurious perturbations in the extracted pulsating signals are removed for smoothening, local minima points in the smoothened pulsating signal are derived, systolic maxima point between two derived local minima are derived, most probable pulse duration and most probable peak-to-peak distance are derived, dicrotic minima is removed while ensuring that every dicrotic minima is preceded by a systolic maxima point and followed by a beat start point of said systolic maxima, diastolic peak is derived while ensuring that every dicrotic maxima is preceded by a diastolic notch followed by next beat start point of that maxima, and physiological parameters are derived from the derived local minima points, systolic maxima points, dicrotic notch and diastolic peak.

Abstract: A method and system for blood pressure (BP) estimation of a person is provided. The system is estimating pulse transit time (PTT) using the ECG signal and PPG signal of the person. A plurality of features are extracted from the PPG. The plurality of PPG features and the PTT are provided as inputs to an automated feature selection algorithm. This algorithm selects a set of features suitable for BP estimation. The selected features are fed to a classifier to classify the database into low/normal BP range and a high BP range. The correctly classified normal BP data are then used to create a regression model to predict BP from the selected features. The current methodology uses automated feature selection mechanism and also employs a block to reject extreme BP data. Thus the available accuracy in predicting BP is expected to be more than the existing BP estimation methods.

Abstract: This disclosure relates generally to biomedical signal processing, and more particularly to method and system for physiological parameter derivation from pulsating signals with reduced error. In this method, pulsating signals are extracted, spurious perturbations in the extracted pulsating signals are removed for smoothening, local minima points in the smoothened pulsating signal are derived, systolic maxima point between two derived local minima are derived, most probable pulse duration and most probable peak-to-peak distance are derived, dicrotic minima is removed while ensuring that every dicrotic minima is preceded by a systolic maxima point and followed by a beat start point of said systolic maxima, diastolic peak is derived while ensuring that every dicrotic maxima is preceded by a diastolic notch followed by next beat start point of that maxima, and physiological parameters are derived from the derived local minima points, systolic maxima points, dicrotic notch and diastolic peak.

Abstract: A method and system for removing corruption in photoplethysmogram (PPG) signals for monitoring cardiac health of patients is provided. The method is performed by extracting photoplethysmogram signals from the patient, detecting and eliminating corruption caused by larger and transient disturbances in the extracted photoplethysmogram signals, segmenting photoplethysmogram signals post detection and elimination of corruption caused by larger and transient disturbances, identifying of inconsistent segments from the segmented photoplethysmogram signals, detecting anomalies from the identified inconsistent segments of the photoplethysmogram signals, analysing the detected anomalies of the photoplethysmogram signals and identifying photoplethysmogram signal segments corrupted by smaller and prolonged disturbances.

Abstract: A method and system of detecting arrhythmia using photoplethysmogram (PPG) signal is provided. The method is performed by extracting photoplethysmogram (PPG) signals from a patient, extracting cardiac parameter from the extracted photoplethysmogram (PPG) signals, identifying presence of cardiac abnormalities as reinforcement filtering of detecting premature ventricular contraction and ventricular flutter from the extracted cardiac parameters, analysing the extracted cardiac parameters to investigate statistical trend and to perform statistical closeness approximation of the extracted photoplethysmogram (PPG) signals and predicting and subsequently classifying type of arrhythmia.