Long-term electrocardiogram and wrist-based photoplethysmogram recordings with annotated atrial fibrillation episodes

Rationale

Atrial fibrillation (AF) has emerged as a worldwide cardiovascular epidemic affecting more than 33 million individuals worldwide and carrying a 5-fold increased risk of brain stroke and a 3-fold increased risk of heart failure (Hindricks et al. 2021). AF is a progressive disease, with primary paroxysmal episodes being self-terminating; therefore, the success of complication management highly depends on early arrhythmia detection, which often requires long-term AF monitoring (Keach et al. 2015). Unfortunately, existing devices for long-term AF monitoring are either expensive (implantable cardiac monitors) or inconvenient due to skin irritation (Holter monitors, electrocardiogram (ECG) patches). Thus, it is desirable to develop inexpensive technologies ensuring wearing comfort. Recently, biooptical photoplethysmography (PPG) signal has emerged as such technology with immense potential for convenient long-term AF monitoring (Pereira et al. 2020). However, due to the lack of guidelines for arrhythmia interpretation in PPG, simultaneous ECG recording is needed for verification of the episodes detected in PPG. The present dataset contains simultaneously acquired wrist-based PPG and reference ECG signals with annotated AF episodes, and thus, is particularly suitable for use in the development and testing of automatic PPG-based AF detectors.

Subjects and data acquisition protocol

The dataset contains long-term ECG and PPG signals from 8 patients with suspected AF monitored for 5 to 8 days (1306 hours in total). Detailed demographic (sex, age, height, weight) and clinical (diagnosed comorbidities, medications) characteristics of the patients are provided in the supplementary file subject_info.xlsx.

The acquisition of the PPG and ECG signals was started at Vilnius University Hospital Santaros Klinikos (Vilnius, Lithuania) and continued for a week at the patient’s home. The PPG signal was acquired at a sampling frequency of 100 Hz using a green LED embedded in a wrist-worn device developed at the Biomedical Engineering Institute (Kaunas, Lithuania). The reference ECG signal was acquired at a sampling frequency of 500 Hz using the Bittium Faros™ 180 ECG device together with the Bittium OmegaSnap™ patch electrode (Oulu, Finland). Additionally, triaxial acceleration signals were acquired with both devices at sampling frequencies of 50 and 25 Hz using wrist-worn and reference ECG devices, respectively. The occurrence times of QRS-complexes in ECG signals were obtained using an open-source QRS detector (Moeyersons et al. 2020), and initial AF episodes were automatically detected using a low-complexity AF detector relying on rhythm irregularity information (Petrėnas et al. 2015). Then, the AF detector output was visually inspected and manually corrected by medical specialists experienced in arrhythmia diagnosis with the aim to find undetected and discard falsely detected episodes.

The data acquisition protocol was in accordance with the ethical principles of the Declaration of Helsinki and was approved by Vilnius Region Biomedical Research Ethics Committee (No. 158200-18/7-1052-557). All patients gave written informed consent to participate.

Technical details

The acquired signals are provided in MAT-files named as follows:

XX_YYY_ZZ,

where XX is the patient ID, YYY is ECG for the signals from the Bittium Faros™ 180 ECG device and PPG for the signals from the wrist-worn device, ZZ is the serial number of the recording. For each patient, there are a single continuous ECG recording and multiple PPG recordings because the acquisition of the PPG could be interrupted for a short time due to technical reasons or for a longer time to allow battery charging of the wrist-worn device.

In addition to the unprocessed PPG, ECG, and acceleration signals, each file contains a signal header, the number of data records within the recording (equivalent to the duration of the recording in seconds), the day when the recording started with respect to the first monitoring day of the patient, and the time of day when the recording started. The ECG files also contain QRS time indices and calculated RR intervals together with AF annotations on a beat-to-beat basis. Each PPG file also contains a vector of timestamps providing the timing (in seconds elapsed since the beginning of the recording) when each data record was logged into the wrist-worn device.

In the subject_info.xlsx file, physical inactivity is defined as < 5000 steps/day or < 150 min/week of moderate-intensity exercise or < 75 min/week of high-intensity exercise, excessive physical activity is defined as > 750 min/week of moderate-intensity exercise, and hypertension is classified into stages based on systolic/diastolic blood pressure: stage I corresponding to 140/90–159/99 mmHg, stage II to 160/100–179/109 mmHg, and stage III to ≥ 180/100 mmHg.

Limitations

When using the resource, researchers should be aware that the PPG and ECG acquisition devices have not been synchronized, and thus, the exact alignment of the signals should be implemented based on some physiological features, e.g., the heart rate obtained from the PPG and ECG signals. Furthermore, the internal clock of the wrist-worn device may slightly drift over the monitoring period, and thus, the provided time of day the PPG recording starts should be treated as a rough indication of the exact time. Users should also be aware that the sampling frequencies of the devices can vary slightly. The effect of this issue on the PPG signal can be mitigated by exploiting the timestamp_sec_data_record vector; however, no such variable is available for the ECG signal.