Reduction of false arrhythmia alarms using signal selection and machine learning.

In this paper, we propose an algorithm that classifies whether a generated cardiac arrhythmia alarm is true or false. The large number of false alarms in intensive care is a severe issue. The noise peaks caused by alarms can be high and in a noisy environment nurses can experience stress and fatigue.

Use of power-line interference for adaptive motion artifact removal in biopotential measurements.

Motion artifacts (MA) have long been a problem in biopotential measurements. Adaptive filtering is widely used for optimal noise removal in many biomedical applications. However, the existing adaptive filtering methods involve the use of additional sensors, limiting the applicability of adaptive filtering for MA reduction.

A Low-Voltage Chopper-Stabilized Amplifier for Fetal ECG Monitoring With a 1.41 Power Efficiency Factor.

This paper presents a low-voltage current-reuse chopper-stabilized frontend amplifier for fetal ECG monitoring. The proposed amplifier allows for individual tuning of the noise in each measurement channel, minimizing the total power consumption while satisfying all application requirements. The low-voltage current reuse topology exploits power optimization in both the current and the voltage domain, exploiting multiple supply voltages (0.

Low-complexity intrauterine pressure estimation using the Teager energy operator on electrohysterographic recordings.

Monitoring the progression of maternal uterine activity provides important prognostic information during pregnancy and parturition. Currently used methods for intrauterine pressure (IUP) measurement are unsuitable for long-term observation of uterine activity. The abdominally measured electrohysterogram (EHG) provides a non-invasive alternative to the existing methods for long-term ambulatory uterine contraction monitoring.

Automated conduction velocity analysis in the electrohysterogram for prediction of imminent delivery: a preliminary study.

Background: Analysis of the electrohysterogram (EHG) is a promising diagnostic tool for preterm delivery. For the introduction in the clinical practice, analysis of the EHG should be reliable and automated to guarantee reproducibility.

Study Goal: Investigating the feasibility of automated analysis of the EHG conduction velocity (CV) for detecting imminent delivery.

Low-complexity R-peak detection for ambulatory fetal monitoring.

Non-invasive fetal health monitoring during pregnancy is becoming increasingly important because of the increasing number of high-risk pregnancies. Despite recent advances in signal-processing technology, which have enabled fetal monitoring during pregnancy using abdominal electrocardiogram (ECG) recordings, ubiquitous fetal health monitoring is still unfeasible due to the computational complexity of noise-robust solutions. In this paper, an ECG R-peak detection algorithm for ambulatory R-peak detection is proposed, as part of a fetal ECG detection algorithm.