wECGdb: An ECG database acquired using a wrist-worn device from patients with acute myocardial infarction and controls

Rationale

According to Eurostat [1], timely interventions could prevent two-thirds of deaths in individuals under 75, with myocardial infarction being the leading cause [2]. Myocardial infarction is typically diagnosed with a 12-lead electrocardiogram (ECG), often unavailable outside the hospital when early symptoms like chest pain occur. Accessible technology for home-based multilead ECG acquisition, followed by automatic ECG analysis or specialist review, may be a promising solution to this problem. Several smartwatches on the market offer ECG functionality; however, they cannot acquire chest ECG leads, which is a crucial drawback, as anterior, septal, and lateral infarctions cannot be diagnosed without such leads.

To address the lack of ECG leads, a wrist-worn wearable device, featuring three electrodes, can be used to enable the simultaneous acquisition of two ECG leads with a single touch (hereafter referred to as a wECG) [3]. One lead is standard lead I, while the other is acquired from chest location, thereby enhancing the amount of cardiac information. However, since the latter lead is non-standard, it poses challenges for interpretation. Therefore, adopting the standard ECG configuration, e.g., through 12-lead ECG synthesis, is essential to present the information in a format that is clinically interpretable.

The wECGdb database contains ECGs simultaneously acquired using a wrist-worn device and a 12-lead ECG for reference. Therefore, it is particularly suitable for the development and testing algorithms for 12-lead ECG synthesis from wECG.

Subjects

The database consists of data from 92 participants, divided into three groups: healthy participants, patients with acute myocardial infarction, and patients with other cardiovascular disease (CVD). To be eligible for inclusion, participants had to be at least 18 years old, without an implanted cardiac device, and without cognitive or linguistic impairments. The acute myocardial infarction group consisted of patients diagnosed with either STEMI or NSTEMI, with ECGs taken within 24 hours of percutaneous coronary intervention. The other CVD group included patients with heart conditions that caused infarction-like changes in the ECG. The healthy group consisted of individuals with no history of heart disease.

The patients were recruited from the inpatient wards of the Cardiology Department at Vilnius University Hospital Santaros Klinikos, Lithuania. All eligible participants provided signed, written informed consent in accordance with the ethical principles outlined in the Declaration of Helsinki. The study was approved by the regional bioethics committee, under reference number 158200-18/7-1052-557.

wECG acquisition

The wrist-worn wearable device, developed at the Biomedical Engineering Institute of Kaunas University of Technology [3, 4], equipped with three bio-potential electrodes, was used to acquire two wECG leads at a single touch. The wECG was acquired at a sampling rate of 500 Hz.

For all participants, the device was positioned slightly above the wrist on the left arm. Lead I, between the left arm (LA) and right arm (RA), was acquired by touching one electrode with the right index finger. The other lead was obtained by placing the electrode on the strap against a specific part of the body.

Acquisition of a standard 12-lead ECG

The standard 12-lead ECG was acquired using disposable Ag/AgCl electrodes at 200 Hz with the Euroholter 12view recorder (Lumed, Italy) and resampled to 500 Hz to match the sampling rate of the wECG.

ECG preprocessing

Both the wECG and the 12-lead ECG were filtered using a high-pass Butterworth filter with a cutoff frequency of 0.5 Hz and a low-pass Parks-McClellan filter with a cutoff frequency of 100 Hz.

Some elderly patients had difficulty maintaining consistent pressure on the electrode, resulting in fewer high-quality wECGs. The database includes only wECGs with acceptable signal quality, as assessed by the consensus beat detection signal quality index [5]. To improve wECG quality, each beat in each lead was replaced by an amplitude-scaled average beat.

Technical details

The wECGdb database is divided into development and test datasets. The development dataset was collected by asking participants to touch specific body sites under clinician guidance: an abdominal site (A), located 2 cm to the left of the umbilicus, and two precordial sites corresponding to the conventional V3 and V5 electrode sites, positioned just below those used to acquire the standard 12-lead ECG. This protocol produced three leads: LA-A, LA-V3, and LA-V5. The test dataset includes only the LA-A lead, which was self-acquired by participants without clinician assistance. Each recording lasted approximately one minute, with at least a one-minute interval between recordings.

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

dataset_XXX, where XXX represents the participant ID. Each dataset_XXX file contains five structures:

• info – Contains participant information, including age, sex, diagnosis (infarction type and location, other CVD), and sampling frequency (Fs);
• LA_A – ECG recordings from the abdomen area with clinician assistance;
• LA_A_self – ECG recordings from the abdomen area without clinician assistance;
• LA_V3 – ECG recordings from the V3 electrode placement area with clinician assistance;
• LA_V5 – ECG recordings from the V5 electrode placement area with clinician assistance.

The LA_A, LA_A_self, LA_V3, and LA_V5 structures include the following variables:

• data_availability – “yes” indicates that the recording meets the quality criteria and is therefore available. “no” indicates that the recording did not meet the quality criteria and is not available.
• wECG – Two ECGs leads acquired using the wrist-worn device;
• reference_12_lead – Simultaneously acquired standard 12-lead ECG;
• ecg_leads – Labels of the ECG leads.

Additionally, the LA_A, LA_V3, and LA_V5 structures contain subdivided recordings into training and testing segments. These segments each include the variables: data, reference_12_lead, and wECG.

The file participant_info.xlsx contains details about the participants, including age, infarction type (STEMI/NSTEMI), presence of bundle branch block (left or right), and information about other CVD.  

wECGdb application example: ECG synthesis using an echo state network

The utility of the wECGdb database is demonstrated through the development of a person-specific model for synthesizing the standard 12-lead ECG from the two-lead wECG. The 12-lead ECG is synthesized using an echo state network – a fixed, sparsely connected, recurrent neural network that functions as a random nonlinear excitable medium to the input wECG [6, 7].  The MATLAB code for designing an echo state network is available in [8].