DOI: 10.5281/zenodo.14975878
title: MSCardio Seismocardiography (SCG) Dataset

description: Overview

The MSCardio Seismocardiography Dataset is an open-access dataset collected as part of the Mississippi State Remote Cardiovascular Monitoring (MSCardio) study. This dataset includes seismocardiogram (SCG) signals recorded from participants using smartphone sensors, enabling scalable, real-world cardiovascular monitoring without requiring specialized equipment. The dataset aims to support research in SCG signal processing, machine learning applications in health monitoring, and cardiovascular assessment.



See the GitHub repository of this dataset for the latest updates: https://github.com/TaebiLab/MSCardio


Background

Cardiovascular diseases remain the leading cause of morbidity and mortality worldwide. SCG is a non-invasive technique that captures chest vibrations induced by cardiac activity and respiration, providing valuable insights into cardiac function. However, the scarcity of open-access SCG datasets has been a significant limitation for research in this field. The MSCardio dataset addresses this gap by providing real-world SCG signals collected via smartphone sensors from a diverse population.

Data Description

Study Population



Total participants enrolled: 123

Participants who uploaded data: 108 (46 males, 61 females, 1 unspecified)

Age range: 18 to 62 years

Total recordings uploaded: 515

Unique recordings after duplicate removal: 502

Platforms used: iOS and Android smartphones


Signal Data



Axial vibrations in three directions (SCG) recorded using smartphone sensors

Sampling frequency varies depending on the device capabilities

Data synchronization is ensured for temporal accuracy

Missing SCG data identified in certain recordings, addressed through preprocessing


Metadata

Each recording includes:



Device model (e.g., iPhone Pro Max)

Recording time (UTC) and time zone

Platform (iOS or Android)

General demographic details (gender, race, age, height, weight)


File Structure

The dataset is organized as follows:

MSCardio_SCG_Dataset/│── info/│   └── all_subject_data.csv   # Consolidated metadata for all subjects│── MSCardio/│   ├── Subject_XXXX/          # Subject-specific folder│   │   ├── general_metadata.json  # Demographic and device information│   │   ├── Recording_XXX/         # Individual recordings│   │   │   ├── scg.csv            # SCG signal data│   │   │   ├── recording_metadata.json  # Timestamp and device details

Data Collection Protocol



Participants placed their smartphone on their chest while lying in a supine position.

The app recorded SCG signals for approximately two minutes.

Self-reported demographic data were collected.

Data were uploaded to the study's cloud storage.


Usage and Applications

This dataset is intended for research in:



SCG signal processing and feature extraction

Machine learning applications in cardiovascular monitoring

Investigating inter- and intra-subject variability in SCG signals

Remote cardiovascular health assessment

The Data_visualization.py script is provided for data visualization


Citation

If you use this dataset in your research, please cite:

@article{rahman2025MSCardio,  author    = {Taebi, Amirtah{\`a} and Rahman, Mohammad Muntasir},  title     = {MSCardio: Initial insights from remote monitoring of cardiovascular-induced chest vibrations via smartphones},  journal   = {Data in Brief},  year      = {2025},  publisher = {Elsevier}}

Contact

For any questions regarding the dataset, please contact:



Amirtahà Taebi and Mohammad Muntasir Rahman

E-mail: ataebi@abe.msstate.edu, mmr510@msstate.edu

Biomedical Engineering Program, Mississippi State University


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This dataset is provided under an open-access license. Please ensure ethical and responsible use when utilizing this dataset for research.