Medical monitoring technologies have gained increasing importance in recent years. Among emerging wearables, in-ear sensing offers a promising alternative to wrist-based devices due to its stable environment and proximity to major arteries, with machine-learning (ML) models showing potential to improve signal analysis performance in this domain, although their design and implementation often lack systematic methodology and reproducibility. This thesis aims to address these gaps by designing an end-to-end in-ear cardiac monitoring system, from custom hardware and dataset collection to the development of a reproducible machine-learning framework for peak detection suitable for embedded deployment. A custom-fit, multi-location in-ear photoplethysmography (PPG) sensing system was developed to collect a multi-activity dataset with a ground-truth electrocardiogram (ECG) reference, enabling systematic evaluation of different Convolutional Neural Network (CNN) architectures for embedded purposes. Results show that signal quality, and thus model performance, strongly depends on sensor placement, with the deep external auditory meatus providing the best signals, followed by the concha. The systematic architecture exploration further revealed consistent design patterns associated with higher accuracy, enabling efficient peak detection with strong ECG correlation. Overall, this work establishes a standardised framework for automatically identifying optimal embedded model architectures for in-ear PPG analysis. Key limitations include the single-subject dataset, computational constraints during model training, and limited final on-device validation. ... Read more

The goal of the WiECG project is to create a prototype device that makes it possible to perform a 12-lead ECG measurement on patients without wires from the patient to a monitor. The solution consists of a transmitter and receiver, one of which is close or on the patients body and the other is connected to a monitor.

This thesis describes the design and implementation of a subsystem of the prototype device that performs digitization, digital processing and reconstruction of the measured 12-lead ECG signal. This concerns converting nine 0 to 3.3V analog signals to the digital domain by using Analog-to-Digital converters, real-time filtering of nine signals with multiple digital IIR filters and reconstructing nine digital signals to the analog domain using Digital-to-Analog converters. Furthermore, component selection, design decisions and the implementation process will be detailed in this document.

The subsystem proposed in this paper is able to successfully sample, efficiently filter and reconstruct nine signals in real time. Recommendations on improving the implementation to better adhere to the lower power requirements for a longer battery life are provided as future research prospects. ... Read more