Electrocardiogram-Capable Smartwatches: Assessing Their Clinical Accuracy and Application

Abstract

Wearable technology has progressed significantly so that today's smartwatches are able to offer advanced health monitoring functions such as electrocardiogram (ECG). This study contains data that can be used to clinically assess the accuracy of four leading smartwatch models: Apple Watch Series 9, Samsung Galaxy Watch 6, Fitbit Sense 2, and Withings ScanWatch. Using the standardized protocol of the International Electrotechnical Commission (IEC 60601-2-25:2011) and a METRON PS-440 patient simulator, controlled ECG signals were generated and recorded to assess smartwatches performance against a reference electrocardiograph (Philips TC30). Parameters such as heart rate (30 to 300 beats per minute), R-wave amplitude (500 to 2000 µV), and ST-segment elevation/depression (-800 to +800 µV) were configured in the patient simulator and recorded 5 times in all devices. This extensive dataset provides valuable information on the feasibility of smartwatches as diagnostic tools, and both their strengths and limitations for clinical and remote cardiovascular monitoring can be observed.

Background

The proliferation of smartwatches, with 165.4 million units sold in 2023 [1], highlights their potential to transform cardiovascular health monitoring. These devices have demonstrated efficacy in detecting Atrial Fibrillation (AF) [2-5], a leading cause of stroke and heart failure. However, broader applications, such as detecting ischemic heart disease or other arrhythmias, remain underexplored. Conventional tools like 12-lead ECGs and Holter monitors, while accurate, are limited by intermittent monitoring and accessibility challenges. This database allows the study of the feasibility of using ECG-capable smartwatches as diagnostic tools, focusing on their compliance with clinical standards and their ability to measure critical ECG parameters beyond Atrial Fibrillation (AF) detection.

Methods

Design of the Evaluation Process

The data aquisition follows the International Electrotechnical Commission Standard IEC 60601-2-25:2011 [6], which specifies requirements for electrocardiographic device accuracy. A METRON PS-440 patient simulator was used to generate synthetic ECG signals representing a wide range of clinically relevant scenarios. The signals were transmitted to both the smartwatches under evaluation and a Philips TC30 electrocardiograph, used as the gold standard.

Signal Transmission and Setup

• Patient Simulator Configuration: The METRON PS-440 was programmed to output calibrated ECG signals, including heart rates from 30 to 300 beats per minute (bpm), R-wave amplitudes from 500 to 2000 µV, and ST-segment elevation/depression ranging from -800 to +800 µV. The simulator’s output connectors followed the AHA and IEC color-coding standards to simplify connection with both the electrocardiograph and smartwatch sensors.

• Smartwatch Signal Acquisition:

  Each smartwatch was mounted on an adjustable stand to ensure stable contact between its sensors and the simulator output. Two flexible Holter cables (1.5 mm DIN) were used:

  • One cable connected the simulator’s R output (right arm) to the smartwatch’s crown or digital button, simulating the negative electrode.

  • The other cable connected the simulator’s L output (left arm) to the back of the smartwatch, simulating the positive electrode.

• Each smartwatch generated two types of output files for each test:

  • PDF files: Graphical representations of the recorded ECG traces, formatted as single-lead (Lead I) electrocardiograms. These files are NOT included in the database.

  • Data files: Raw numerical data, such as time-series voltage measurements, heart rate calculations, and other derived parameters. Only the raw data in WDFB is included in the database.

• Electrocardiograph Signal Acquisition: The Philips TC30 was connected to the simulator using standard 12-lead ECG cables. Signals were recorded for 10-second intervals per lead, capturing all standard ECG derivations. Calibration was verified prior to each test to ensure the electrocardiograph met accuracy requirements (±5% or ±40µV).

Data Description

This database contains raw data, transformed to WDFB format, generated using a Patient Simulator, the METRON PS-440, and measured with 5 different devices: (i) hospital electrocardiograph model Philips TC30; (ii) Apple Watch Series 9; (iii) Samsung Galaxy Watch 6; (iv) Fitbit Sense 2; and (v) Withings ScanWatch.

The database is organised using a directory for each device:

1. philips_tc30: Raw data in WDFB format for the Philips TC30 hospital electrocardiograph.
2. applewatch_serie8: Raw data in WDFB format for Apple Watch Series 9.
3. samsunggalaxy6: Raw data in WDFB format for the Samsung Galaxy Watch 6.
4. fitbitsense2: Raw data in WDFB format for the Fitbit Sense 2.
5. withingsscanwatch: Raw data in WDFB format for Withings ScanWatch.

For each device the database contains the following records, all experiments in quintuplicate:

• amp_test: 4 experiments
  • amp500: records amp500_0 to amp500_4 corresponding to an ECG signal of 500µV amplitude.
    ...
  • amp2000: records amp2000_0 to amp2000_4 corresponding to an ECG signal of 2000µV amplitude.

• freq_test: 15 experiments
  • f30: records f30_0 to f30_4 corresponding to a 30 BPM ECG signal.
    ...
  • f300: records f300_0 to f300_4 corresponding to a 300 BPM ECG signal.

• sqr-2hz: sqr-2hz_0 to sqr-2hz_4 registers corresponding to a square wave signal with a frequency of 2 Hz.

• st-segment: 16 experiments
  • st-m8: st-m8_0 to st-m8_4 records corresponding to an ECG signal with ST-segment depression of -800µV.
    ...
  • st-p8: st-p8_0 to st-p8_4 records corresponding to an ECG signal with ST-segment elevation of +800µV.

The database contains a total of 915 records.

Usage Notes

From a clinical perspective, smartwatches offer a reliable monitoring option for the detection of common bradyarrhythmias and tachyarrhythmias. Among the devices evaluated, the Apple Watch Series 9, Fitbit, and Withings ScanWatch perform most accurately been able to automatically detect a heart rate range of 40 to 220 beats per minute (bpm), which covers most situations in healthy individuals with moderate to intense physical activity. On the other hand, the Samsung Galaxy 6 has a less reliable performance, being accurate only for low heart rates, but with significant errors in detecting from 100 beats per minute (bpm) and above. This makes it less suitable for measuring high heart rates, such as those that may occur during intense exercise or in cases of tachycardia.

Cardiomyopathy detection could be a potential use for these devices, as the measurement of R-wave amplitude and morphology is quite accurate. A relevant clinical application of these devices is the diagnosis and monitoring of ischemic heart disease, thanks to its accuracy in measuring changes in the J-point. For J-point detection, the Apple Watch stands out for its low variability and errors. The Fitbit also shows uniformity in its J-point measurements. Even so, its variability is homogeneous, offering acceptable stability and being a suitable option for tracking, albeit with less accuracy than the Apple Watch. On the other hand, the Samsung Galaxy 6 and Withings ScanWatch are the least recommended for J-point detection due to their high variability and significant errors, which reduces their accuracy and reliability for clinical diagnosis.

For square wave signal detection, the Apple Watch stands out as one of the most accurate devices, capturing the signal with slight ripples of less than 1 mm, indicating that they possibly use soft filtering and adequate measurement continuity. The performance of the Fitbit is also remarkable, although it presents some elongation at the beginning of the transitions (around 2 mm) probably due to an aggressive low pass filter, which may distort the signal morphology, but maintains an acceptable level of accuracy. The Samsung shows high frequency oscillations with significant amplitude, evidencing problems in the correct capture of the ECG signal. The Withings shows low frequency oscillations and a possible use of a narrow bandpass filter that alters the signal, compromising the accuracy of the measurement. As for the response to sine wave signals, both the Apple Watch and the Fitbit show good performance within an acceptable frequency range.

In summary, Apple and Fitbit devices show the highest potential for cardiomyopathy detection and monitoring of ischemic heart disease, standing out for their accuracy in measuring R-wave amplitude, J-point and sine wave signals. The Apple Watch is the most accurate, with low variability in all aspects evaluated, while the Fitbit also offers solid performance, albeit with lower accuracy in J-point detection. On the other hand, the Samsung Galaxy Watch 6 presents limitations in the detection of square wave signals, showing oscillations of high frequency that affect the accurate capture of the ECG signal. In addition, its performance in heart rate monitoring above 100 beats per minute (bpm) is unreliable, with significant errors, making it less suitable for high heart rates or intense exercise. Finally, the Withings ScanWatch is less recommended due to its higher variability and frequent errors in R-wave, J-point and square wave signal detection, compromising its reliability in clinical applications.

The information contained in this database has been used for the following publication (pending revision):

Buelga Suárez ML, Recas J, González-Cabeza S, Sanz-Guerrero M, Diaz-Vicente M, Rebolleda Sánchez A, Piñuel L, Alonso Salinas GL. "Performance Evaluation of Smartwatches: Can They Match Clinical Standards for ECG Analysis?"

Ethics

Data collected from synthetic sources

Acknowledgements

This study is partially funded by the Instituto Ramón y Cajal de Investigación Sanitaria.

Conflicts of Interest

The authors declare that they have no conflict of interest.

References

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