Khalifa University

Ahsan Khandoker

Hani Hasan Mustafa Saleh

Mohammed Ismail Elnaggar

Nourhan Yahya Bayasi

Temesghen Tekeste Habte

Baker Mohammad

Mahmoud A Al-Qutayri

Methods for monitoring a fetus employ a mobile computing device. A method of monitoring a fetus includes generating a first preprocessed acoustic signal via amplification of a first acoustic signal. The first preprocessed acoustic signal is processed by a mobile computing device to extract one or more of a fetal heart sound, a fetal heartbeat rate, or a fetal heartbeat acoustic intensity.

Method of monitoring a fetus

A fetal phonocardiogram system featuring an array of acoustic sensors designed to be temporarily affixed to a pregnant patient to convert the vibrations associated with a fetal heartbeat and/or fetal movement into a signal. The signal can be amplified and transferred to a processing element for filtering non-fetal sounds in order to resolve the fetal heartbeat. The filtered signal can be used in conjunction with an array of multiple acoustic sensors to locate a fetal position relative to the array.

Low cost fetal phonocardiogram

A medical device and method for detecting a ventricular arrhythmia event is disclosed. The medical device includes input circuitry configured to receive an electrocardiogram (ECG) signal and processing circuitry coupled to the input circuitry that is configured to identify fiducial points within the ECG signal. Feature extraction circuitry coupled to the processing circuitry is configured to determine interval variability between the fiducial points. Machine learning circuitry is coupled to the feature extraction circuitry and is configured to detect ventricular arrhythmia based on the interval variability between the fiducial points.

Medical device and method for detecting a ventricular arrhythmia event

A medical device having automated electrocardiogram (ECG) feature extraction is disclosed. The medical device includes input circuitry configured to receive an ECG signal. Processing circuitry coupled to the input circuitry is configured to identify at least one fiducial point of heartbeat signature of the ECG signal. The processing circuitry is further configured to perform substantially simultaneously both a discrete wavelet transform (DWT) and a curve length transform (CLT) to identify the at least one fiducial point.

Medical device having automated ECG feature extraction

A medical device and method for detecting a ventricular arrhythmia event is disclosed. The medical device includes input circuitry configured to receive an electrocardiogram (ECG) signal, processing circuitry coupled to the input circuitry and configured to identify at least one fiducial point of a first heartbeat signature and at least fiducial point of a second heartbeat signature of the ECG signal, and feature extraction circuitry coupled to the processing circuitry. The feature extraction circuitry is configured to determine at least one difference between the at least one fiducial point of the first heartbeat signal and the at least one fiducial point of the second heartbeat signal. Machine learning circuitry is coupled to the feature extraction circuitry and is configured to select a ventricular arrhythmia class based on the at least one difference.

Growing community of inventors

Abu Dhabi, United Arab Emirates

Ahsan Khandoker