Life Stores #4 - Pacemaker Pulse Detection

At the start of my employment, I was given the task of taking an existing prototype pacemaker pulse algorithm and implementing it into a DSP to run in an embedded environment. Also, I was to improve on the already great performance of the algorithm.

Like most of the projects that I start, I was unfamiliar with the problem domain and the target technology. I needed to learn about some of the fundamentals of digital signal processing, the domain of diagnostic cardiology, and pacemakers themselves.

I started by examining the data that we received from a clinical investigation with our new investigational device. I quickly discovered that I needed to invent a filter that would remove the injected lead fail detection carrier signal, before I could view the ECG with the pacemaker pulses. Once I completed that task, I was able to review the data.

After reviewing the data, I reviewed the original prototype algorithm and ported it into my system, at a higher sampling rate. The original algorithm only worked on uniphasic pacemaker pulses, and it could not handle biphasic pacemaker pulses. I started working on adapting the algorithm that could handle biphasic pacemaker pulses.

A pacemaker pulse can be modeled as a Markov process, with a basic left to right model. I created a deterministic finite state machine to handle the different states of the Markov process, and I used fixed criteria for the transitions from state to state. Once I completed the uniphasic model, I adapter for the biphasic case. Then, I completed a series of experiments where I adjusted the initial design logic to match the training data set. Once, I had the algorithm working well over the training data set, I completed testing on the test data set. Further adjustments to the FSA were required, to account for specific noise conditions, to account for rare pacemaker pulse morphologies, and to account for performance issues.

The final algorithm detects pacemaker pulses from atrial, ventricular and biventricular sources, with the desired improvement in sensitivity and positive predictive accuracy for pacemaker pulse detection. The clinical validation of the algorithm showed an increased sensitivity when compared to the legacy system.