FRAME-C: A knowledge-augmented deep learning pipeline for classifying multi-electrode array electrophysiological signals

-- Amyotrophic lateral sclerosis (ALS) is a fatal neu-rodegenerative disorder characterized by motor neuron degeneration, with alterations in neural excitability serving as key indicators. Recent advancements in induced pluripotent stem cell (iPSC) technology have enabled the generation of human iPSC-derived neuronal cultures, which, when combined with multi-electrode array (MEA) electrophysiology, provide rich spatial and temporal electrophysiological data. Traditionally, MEA data is analyzed using handcrafted features based on potentially imperfect domain knowledge, which while useful may not fully capture all the useful characteristics inherent in the MEA data. Machine learning, in particular deep learning has the potential to automatically learn relevant characteristics (features) from raw data, without solely relying on handcrafted feature extraction. However, handcrafted features remain critical for encoding domain knowledge and improving model interpretability, especially in scenarios with limited or noisy data, as is often the case in most experimental studies. This study introduces FRAME-C, a knowledge-augmented machine learning pipeline that combines domain knowledge, raw spike waveform data, and deep learning techniques to classify MEA signals and identify ALS-specific phenotypes. FRAME-C leverages deep learning to learn important features from spike waveforms, while also incorporating handcrafted features such as spike amplitude, inter-spike interval, and spike duration, thus preserving key spatial and temporal information. We validate FRAME-C on both simulated and real-world MEA data from human iPSC-derived neuronal cultures, demonstrating its superior performance compared to existing methods for MEA classification. FRAME-C performs significantly better, showing more than a 11% improvement on real-world data and up to 25% improvement on simulated data in terms of the test accuracy. Moreover, we show that FRAME-C can be used to evaluate the importance of each of the handcrafted features, and thereby contributing to the interpretation of the classification results. Permutation feature importances are calculated for these handcrafted features, providing further insights into the phenotypes of ALS. Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that leads to a progressive loss of motor neurons. At the onset of ALS, symptoms may include limb weakness and difficulty in swallowing. However, the disease invariably progresses towards paralysis and respiratory failure within three to five years [1]. A small portion of ALS patients (5 - 10%) are familial (fALS) in nature and can be linked to a family history of ALS. However, the majority (90 - 95%) are sporadic (sALS) and do not have any known family history.