Analysis and postprocessing of ECG or heart rate data from wearable devices beyond the proprietary cloud and app infrastructure of the vendors

Figures

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  Figure 1. Wearable electrocardiographic (ECG) recorders. From left to right: ECG watches: Samsung Galaxy Watch 3, FitBit Sense, and Withings ScanWatch with a medical graded oximeter for SpO2 measures. Right of the ScanWatch is the NextSENSE Pulse sensor 1-lead ECG recorder, which attaches to the chest by a belt or through electrodes. Displayed on the center smartphone is a wirelessly transmitted ECG signal, recorded with the Pulse sensor. Next are 2 different AliveCor Kardia devices: a 6-lead recorder—the KardiaMobile 6L (top), and a 1-lead recorder—the KardiaMobile (bottom). Right: Apple Watch with ECG recording capabilities displayed on a paired iPhone.

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  Figure 2. The 3 scenarios used in this research cover electrocardiographic (ECG) recordings to postprocessing of the ECG signals or R-R intervals. The first scenario uses the vendor-proprietary application (APP) with a self-made application programming interface (API) software to handle the ECG PDF recordings. The second scenario uses the vendor’s APP and API for consecutive postprocessing of raw ECG and R-R interval data with self-made software. The third scenario is based on raw-data access by a self-made acquisition and postprocessing software APP supported by software development kit (SDK), software library, or API provided by the vendors. HR = heart rate.

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  Figure 3. Samples of electrocardiographic recordings (right) using the Apple Watch (top left) and KardiaMobile 6L (bottom left).

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  Figure 4. ECG QTc Calculator overview. From bottom left: The patient applies a mobile recording device to record the electrocardiographic (ECG) signal and the application (APP) sends the ECG as a PDF to the physician. The physician can then postprocess the ECG with ECG QTc Calculator to obtain the QT and QTc measurements.

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  Figure 5. Self-made demonstrator application (APP) running in a Web browser for processing of uploaded electrocardiographic (ECG) PDF files. The APP is designed for physicians to measure the QT intervals of the ECG lead on the PDF and calculate the corrected QT interval. QTcB = Bazett formula; QTcH = Hodges formula.

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  Figure 6. Continuous 1- lead ECG recorded with a Bluetooth electrocardiographic (ECG) sensor on the chest (top left). A smartphone application (APP) displays the data during recording (top middle) and uploads the data to the cloud-based medical device data system (topright). When the physician needs to monitor the QT-interval duration for QT safety, he or she can open a Web page and measure the recorded ECG with mouse-based calipers immediately after the ECG is recorded anywhere in the world (bottom).

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  Figure 7. Raw data from a Bluetooth low-energy (BLE) heart rate sensor is received by 2 different software methods. Right: New Web-based application programming interface (API) displays the heart rate data online in a modern HTML5 Web browser. Left: Heart rate sensor is accessed via Apple’s Core Bluetooth API, and an Apple Swift program calculates heart rate and heart rate variability data online.

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  Figure 8. Custom-made demonstrator application (APP) written with the Microsoft Band 2 software development kit. The APP displays multiple sensor data retrieved from the Microsoft Band 2. Unfortunately, this powerful wearable is no longer available.