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

Nisal Ranasinghe, Dzung Do-Ha, Simon Maksour, Tamasha Malepathirana, Sachith Seneviratne, Lezanne Ooi, Saman Halgamuge

TL;DR

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, demonstrating superior performance over existing classification methods.

Abstract

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative 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 useful characteristics inherent in the data. Machine learning, particularly deep learning, has the potential to automatically learn relevant characteristics from raw data without solely relying on handcrafted feature extraction. However, handcrafted features remain critical for encoding domain knowledge and improving interpretability, especially with limited or noisy data. 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 incorporating handcrafted features such as spike amplitude, inter-spike interval, and spike duration, preserving key spatial and temporal information. We validate FRAME-C on both simulated and real MEA data from human iPSC-derived neuronal cultures, demonstrating superior performance over existing classification methods. FRAME-C shows over 11% improvement on real data and up to 25% on simulated data. We also show FRAME-C can evaluate handcrafted feature importance, providing insights into ALS phenotypes.

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

TL;DR

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, demonstrating superior performance over existing classification methods.

Abstract

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative 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 useful characteristics inherent in the data. Machine learning, particularly deep learning, has the potential to automatically learn relevant characteristics from raw data without solely relying on handcrafted feature extraction. However, handcrafted features remain critical for encoding domain knowledge and improving interpretability, especially with limited or noisy data. 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 incorporating handcrafted features such as spike amplitude, inter-spike interval, and spike duration, preserving key spatial and temporal information. We validate FRAME-C on both simulated and real MEA data from human iPSC-derived neuronal cultures, demonstrating superior performance over existing classification methods. FRAME-C shows over 11% improvement on real data and up to 25% on simulated data. We also show FRAME-C can evaluate handcrafted feature importance, providing insights into ALS phenotypes.

Paper Structure

This paper contains 32 sections, 4 equations, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Dataset
  4. Human iPSC-derived neuronal culture data
  5. Motor neuron differentiation
  6. Microelectrode array measurements
  7. Dataset exploration
  8. Simulated data
  9. Methods
  10. Noise filtering and time split
  11. Spike waveform extraction
  12. Knowledge augmentation using spike features
  13. Spike amplitude
  14. inter-spike interval (ISI)
  15. Spike duration / spike width
  16. ...and 17 more sections

Figures (4)

  • Figure 1: The distribution of the mean firing rates of all the MEA signals recorded from human iPSC derived neuronal cultures. The MFR is shown on a log scale.
  • Figure 2: The architecture of the proposed FRAME-C pipeline. The raw spike waveforms are augmented using handcrafted features based on domain knowledge. The sequences of spikes are truncated / padded to a fixed length of $len_{spikes}$, while the sequences of bursts are truncated / padded to a fixed length of $len_{bursts}$
  • Figure 3: The UMAP visualization of one of the ALS datasets, colored by the well number from where the MEA signal was recorded from. Some clusters are visible, which indicates that some batch effects are present in the dataset
  • Figure 4: The figure shows the change in test accuracy for each model versus the age of the neuronal culture