Electrocardiography (ECG)

Local differential bipolar sensor, designed for single-lead ECG acquisitions.

Our low-noise ECG local differential triode configuration enables fast application and unobtrusive single-lead ECG data acquisition (although custom electrode cable configurations are available). The state-of-the-art design of the analog frontend on this sensor is specifically targeted at analyzing minutiae in the data and provides medical-grade raw sensor data.

This sensor can be used to extract heart rate data and other ECG features, enabling its application in research fields such as biomedical, biofeedback, psychophysiology, and sports, among many others.

Features
  • Bipolar differential measurement
  • Pre-conditioned analog output
  • High signal-to-noise ratio
  • Medical-grade raw data output
  • Ready-to-use & miniaturized form-factor
Gain 1019
Bandwidth 25-100Hz
CMRR 100dB
Connector Type UC-E6 (Male)
Range ±1.47mV (with VCC=3V)
Input Impedance >100GOhm
Cable Length 100cm±0.5cm (customizable)

This sensor is not a standalone use sensor and requires the use of a biosignalsplux hub and electrodes in order to acquire any data. It can be included in the following biosignalsplux kits which come with all the needed hardware and accessories for ECG data acquisition:

Heart Rate Variability (HRV) Analysis

The Heart Rate Variability (HRV) add-on is designed to process Electrocardiography (ECG) and Blood Volume Pulse (BVP) sensor data and to extract Beat-to-Beat interval series from which temporal, spectral, and nonlinear HRV parameters are computed. All the algorithms were implemented according to the "Standards of Measurement, Physiological Interpretation, and Clinical Use" devised by the joint European Society of Cardiology and North American Society of Pacing Electrophysiology task force.

Find Out More


T. Choksatchwathi, P. Ponglertnapakorn, A. Ditthapron, P. Leelaarporn, T. Wisutthisen, T. Wilaiprasitporn, Improving Heart Rate Estimation on Consumer Grade Wrist-Worn Device Using Post-Calibration Approach, in IEEE Sensors Journal, 2020

G. Ramos, J. Vaz, G. Mendonça, P. Pezarat-Correia, J. Rodrigues, M. Alfaras, H. Gamboa, Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor, in Journal of Healthcare Engineering, vol. 2020, no. 6484129, pp. 1-18, 2020

B. Lutz, M. Adam, S. Feuerriegel, N. Prölloch, D. Neumann, Affective Information Processing of Fake News: Evidence from NeuroIS, in Information Systems and Neurscience, pp. 121-128, 2019

A. Reiss, I. Indlekofer, P. Schmidt, K. van Laerhoven, Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks, in Sensors, vol. 19, no. 14:3079, pp. 1-27, 2019

D. Lernia, P. Cipresso, E. Pedroli, G. Riva, Toward an embodied medicine: a portable device with programmable interoceptive stimulation for heart rate variability enhancement, in Sensors, vol. 18, no. 2469, pp. 1-13, 2018

Looking for more?

Visit the biosignalsplux publications page for a full list of available publications.

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