Orthonormal-Basis Partitioning and Time-Frequency Representation of Cardiac Rhythm Dynamics

Personalized ECG monitoring and adaptive machine learning

This non-technical review introduces key concepts in personalized ECG monitoring (pECG), which aims to optimize the detection of clinical events and their warning signs as well as the selection of alarm thresholds. We review several pECG methods, including anomaly detection and adaptive machine learning (ML), in which learning is performed sequentially as new data are collected. We describe a distributed-network multiscale pECG system to show how the computational load and time associated with adaptive ML could be optimized. In this architecture, the limited analysis of ECG waveforms is performed locally (e.g., on a smart phone) to determine a small number of clinically important ECG elements, and an adaptive ML engine is located on a remote server (Internet cloud) to determine an individual's "fingerprint" basis patterns and to detect anomalies in those patterns.

Early prediction of the highest workload in incremental cardiopulmonary tests

Incremental tests are widely used in cardiopulmonary exercise testing, both in the clinical domain and in sport sciences. The highest workload (denoted W peak ) reached in the test is key information for assessing the individual body response to the test and for analyzing possible cardiac failures and planning rehabilitation, and training sessions. Being physically very demanding, incremental tests can significantly increase the body stress on monitored individuals and may cause cardiopulmonary overload. This article presents a new approach to cardiopulmonary testing that addresses these drawbacks. During the test, our approach analyzes the individual body response to the exercise and predicts the W peak value that will be reached in the test and an evaluation of its accuracy. When the accuracy of the prediction becomes satisfactory, the test can be prematurely stopped, thus avoiding its entire execution. To predict W peak , we introduce a new index, the CardioPulmonary Efficiency Index (CPE), summarizing the cardiopulmonary response of the individual to the test. Our approach analyzes the CPE trend during the test, together with the characteristics of the individual, and predicts W peak . A K-nearest-neighbor-based classifier and an ANN-based classier are exploited for the prediction. The experimental evaluation showed that the W peak value can be predicted with a limited error from the first steps of the test.

Increasing sensitivity in the measurement of heart rate variability: the method of non-stationary RR time-frequency analysis

A novel method of the time-frequency analysis of non-stationary heart rate variability (HRV) is developed which introduces the fragmentary spectrum as a measure that brings together the frequency content, timing and duration of HRV segments. The fragmentary spectrum is calculated by the similar basis function algorithm. This numerical tool of the time to frequency and frequency to time Fourier transformations accepts both uniform and non-uniform sampling intervals, and is applicable to signal segments of arbitrary length. Once the fragmentary spectrum is calculated, the inverse transform recovers the original signal and reveals accuracy of spectral estimates. Numerical experiments show that discontinuities at the boundaries of the succession of inter-beat intervals can cause unacceptable distortions of the spectral estimates. We have developed a measure that we call the "RR deltagram" as a form of the HRV data that minimises spectral errors. The analysis of the experimental HRV data from real-life and controlled breathing conditions suggests transient oscillatory components as functionally meaningful elements of highly complex and irregular patterns of HRV.