PhysioZoo documentation

Introduction

PhysioZoo is a collaborative platform dedicated to the study of the heart rate variability (HRV) from Humans and other mammals’ electrophysiological recordings. The main components of the platform are:

  • Software

    • An open-source algorithmic toolbox for matlab (mhrv), which implements all standard HRV analysis algorithms, a selection of peak detection algorithms and prefiltering routines. This can be used within your own data analysis code using the mhrv API.

    • An open-source graphical user interface (PZ-UI) that provides a user friendly interface for advanced HRV analysis of RR-intervals time series and data visualization tools. This enables easy access to HRV analysis without writing any code.

  • Databases

    • A set of annotated databases (PZ-DB) of electrophysiological signals from different mammals (dog, rabbit and mouse). Available here.

    • Manually audited peak locations and signal quality annotations for each of the recordings.

  • Configuration

    • A set of configuration files that adapt the HRV measures and mhrv algorithms to work with data from different mammals.

    • All HRV measures can be further adapted for the analysis of other mammals by creating simple human-readable mammal-specific configuration files.

Note

The PZ-UI user interface has two modules: a Peak detection (used to process electrophysiological signals and obtain the RR time series) module and a HRV analysis module (used to process the RR time series and compute HRV measures).

The PhysioZoo mission is to standardize and enable the reproducibility of HRV analysis in mammals’ electrophysiological data. This is achieved through its open source code, freely available user interface and open access databases. It also aims to encourage the scientific community to contribute their electrophysiological databases and novel HRV algorithms/analysis tools for advancing the research in the field.

Feedback on how to improve the PhysioZoo platform is welcomed. Do not hesitate to drop us an email at:

physiozoolab@gmail.com

Source code, data or interface enhancement contributions are welcome. Look here on how to contribute to PhysioZoo.

Please include the standard citation to PhysioZoo when using the resources available on the platform:

Joachim A. Behar*, Aviv A. Rosenberg*, Ido Weiser-Bitoun, Ori Shemla, Alexandra Alexandrovich, Evgene Konyukhov, Yael Yaniv. 2018. ‘PhysioZoo: a novel open access platform for heart rate variability analysis of mammalian electrocardiographic data.’ Accepted for publication in Frontiers in Physiology.

*Equal contribution.

mhrv toolbox documentation

mhrv is a matlab toolbox for calculating Heart-Rate Variability (HRV) metrics from both ECG signals and RR-interval time series. The toolbox works with ECG data in the PhysioNet WFDB data format.

  • WFDB wrappers and helpers. A small subset of the PhysioNet WFDB tools are wrapped with matlab functions, to allow using them directly from matlab.

  • ECG signal processing. Peak detection and RR interval extraction from ECG data in PhysioNet format.

  • RR-intervals signal processing. Ectopic beat rejection, frequency filtering, nonlinear dynamic and fractal analysis.

  • HRV Metrics. Calculating quantitative measures that indicate the activity of the heart based on RR intervals using all standard HRV metrics defined in the literature.

  • Configuration. The toolbox is fully configurable with many user-adjustable parameters. Everything can be configured either globally with human readable YAML config files, or when calling the toolbox functions via matlab style key-value pair arguments.

  • Plotting. All toolbox functions support plotting their output for data visualization. The plotting code is separated from the algorithmic code in order to simplify embedding this toolbox in other matlab applications.

  • Top-level analysis functions. These functions work with PhysioNet records and allow streamlined HRV analysis by composing the functions of this toolbox. Supports multi-record batch analysis for calculating HRV features of large datasets.

Working with the toolbox

Indices and tables