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Objective. Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts. Approach. In this work, we propose Power-MF , a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmark Power-MF against three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA). Main results. Our results show that Power-MF outperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5% ± 0.5% F1-score, B2 Labour: 98.0% ± 3.0% F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise. Significance. Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible., (Creative Commons Attribution license.)

In the last decade the interest for heart rate variability analysis has increased tremendously. Related algorithms depend on accurate temporal localization of the heartbeat, e.g. the R-peak in electrocardiogram signals, especially in the presence of arrhythmia. This localization can be delivered by numerous solutions found in the literature which all lack an exact specification of their temporal precision. We implemented three different state-of-the-art algorithms and evaluated the precision of their R-peak localization. We suggest a method to estimate the overall R-peak temporal inaccuracy-dubbed beat slackness-of QRS detectors with respect to normal and abnormal beats. We also propose a simple algorithm that can complement existing detectors to reduce this slackness. Furthermore we define improvements to one of the three detectors allowing it to be used in real-time on mobile devices or embedded hardware. Across the entire MIT-BIH Arrhythmia Database, the average slackness of all the tested algorithms was 9ms for normal beats and 13ms for abnormal beats. Using our complementing algorithm this could be reduced to 4ms for normal beats and to 7ms for abnormal beats. The presented methods can be used to significantly improve the precision of R-peak detection and provide an additional measurement for QRS detector performance.

Medical diagnosis is the first level for recognition and treatment of diseases. To realize fast diagnosis, we propose a concept of a basic framework for the underwater monitoring of a diver's ECG signal, including an alert system that warns the diver of predefined medical emergency situations. The framework contains QRS detection, heart rate calculation and an alert system. After performing a predefined study protocol, the algorithm's accuracy was evaluated with 10 subjects in a dry environment and with 5 subjects in an underwater environment. The results showed that, in 3 out of 5 dives as well as in dry environment, data transmission remained stable. In these cases, the subjects were able to trigger the alert system. The evaluated data showed a clear ECG signal with a QRS detection accuracy of 90 %. Thus, the proposed framework has the potential to detect and to warn of health risks. Further developments of this sample concept can imply an extension for monitoring different biomedical parameters.

The electrocardiogram (ECG) is a key diagnostic tool in heart disease and may serve to detect ischemia, arrhythmias, and other conditions. Automatic, low cost monitoring of the ECG signal could be used to provide instantaneous analysis in case of symptoms and may trigger the presentation to the emergency department. Currently, since mobile devices (smartphones, tablets) are an integral part of daily life, they could form an ideal basis for automatic and low cost monitoring solution of the ECG signal. In this work, we aim for a realtime classification system for arrhythmia detection that is able to run on Android-based mobile devices. Our analysis is based on 70% of the MIT-BIH Arrhythmia and on 70% of the MIT-BIH Supraventricular Arrhythmia databases. The remaining 30% are reserved for the final evaluation. We detected the R-peaks with a QRS detection algorithm and based on the detected R-peaks, we calculated 16 features (statistical, heartbeat, and template-based). With these features and four different feature subsets we trained 8 classifiers using the Embedded Classification Software Toolbox (ECST) and compared the computational costs for each classification decision and the memory demand for each classifier. We conclude that the C4.5 classifier is best for our two-class classification problem (distinction of normal and abnormal heartbeats) with an accuracy of 91.6%. This classifier still needs a detailed feature selection evaluation. Our next steps are implementing the C4.5 classifier for Android-based mobile devices and evaluating the final system using the remaining 30% of the two used databases.