Abstract

This work will present a comparison of several Artificial Neural Network methods for a classification problem related to cardiac safety assessment. Given the extracellular field potential recorded by means of micro-electrode arrays, the aim is to determine whether a given chemical drug is altering the electrical activity of cardiomyocytes by disrupting the normal behavior of the hERG channels. To do so, this work has considered four different Neural Network methods and compared them in terms of accuracy and computational costs. The conclusion is that, among the tested architectures, the Multilayer Perceptron (MLP) and multivariate 1-dimensional Convolutional Neural Network (1D-CNN) give the most promising results.

References

• Nicola Ferri et al. “Drug attrition during pre-clinical and clinical development: Understanding and managing druginduced cardiotoxicity”. In: Pharmacology and Therapeutics 138.3 (2013), pp. 470–484. ISSN: 0163-7258. DOI: https://doi.org/10.1016/j.pharmthera.2013. 03.005.
• Bernard Fermini and Anthony A Fossa. “The impact of drug-induced QT interval prolongation on drug discovery and development”. In: Nature reviews Drug discovery 2.6 (2003), pp. 439–447.
• Antje D Ebert, Ping Liang, and Joseph C Wu. “Induced pluripotent stem cells as a disease modeling and drug screening platform”. In: Journal of cardiovascular pharmacology 60.4 (2012), p. 408.
• Fabien Raphel et al. “A greedy classifier optimization strategy to assess ion channel blocking activity and proarrhythmia in hiPSC-cardiomyocytes”. In: PLoS computational biology 16.9 (2020), e1008203.
• Nils Strodthoff et al. “Deep learning for ECG analysis: Benchmarks and insights from PTB-XL”. In: IEEE Journal of Biomedical and Health Informatics 25.5 (2020), pp. 1519–1528.
• Amin Ullah et al. “Classification of arrhythmia by using deep learning with 2-D ECG spectral image representation”. In: Remote Sensing 12.10 (2020), p. 1685.
• Alexander Craik, Yongtian He, and Jose L Contreras-Vidal. “Deep learning for electroencephalogram (EEG) classification tasks: a review”. In: Journal of neural engineering 16.3 (2019), p. 031001.
• U Rajendra Acharya et al. “Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals”. In: Computers in biology and medicine 100 (2018), pp. 270–278.
• Sophie Kussauer, Robert David, and Heiko Lemcke. “hiPSCs derived cardiac cells for drug and toxicity screening and disease modeling: what micro-electrode-array analyses can tell us”. In: Cells 8.11 (2019), p. 1331.
• Guang Deng and LW Cahill. “An adaptive Gaussian filter for noise reduction and edge detection”. In: 1993 IEEE conference record nuclear science symposium and medical imaging conference. IEEE. 1993, pp. 1615–1619.
• Vinod Nair and Geoffrey E Hinton. “Rectified linear units improve restricted boltzmann machines”. In: Icml. 2010.
• Yingying Wang et al. “The influence of the activation function in a convolution neural network model of facial expression recognition”. In: Applied Sciences 10.5 (2020), p. 1897.
• Bing Xu et al. “Empirical evaluation of rectified activations in convolutional network”. In: arXiv preprint arXiv:1505.00853 (2015).
• Jun Han and Claudio Moraga. “The influence of the sigmoid function parameters on the speed of backpropagation learning”. In: International workshop on artificial neural networks. Springer. 1995, pp. 195–201.
• Shibani Santurkar et al. “How does batch normalization help optimization”. In: Advances in neural information processing systems 31 (2018).
• Sergey Ioffe and Christian Szegedy. “Batch normalization: Accelerating deep network training by reducing internal covariate shift”. In: International conference on machine learning. PMLR. 2015, pp. 448–456.
• Marius-Constantin Popescu et al. “Multilayer perceptron and neural networks”. In: WSEAS Transactions on Circuits and Systems 8.7 (2009), pp. 579–588.
• Gaurav Kumar, Urja Pawar, and Ruairi O’Reilly. “Arrhythmia Detection in ECG Signals Using a Multilayer Perceptron Network.” In: AICS. 2019, pp. 353–364.
• Aston Zhang et al. “Dive into deep learning”. In: arXiv preprint arXiv:2106.11342 (2021).
• Weili Guo et al. “Theoretical and numerical analysis of learning dynamics near singularity in multilayer perceptrons”. In: Neurocomputing 151 (2015), pp. 390–400.
• Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
• Kunihiko Fukushima and Sei Miyake. “Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition”. In: Competition and cooperation in neural nets. Springer, 1982, pp. 267–285.
• Yann LeCun et al. “Object recognition with gradient-based learning”. In: Shape, contour and grouping in computer vision. Springer, 1999, pp. 319–345.
• Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. “Imagenet classification with deep convolutional neural networks”. In: Advances in neural information processing systems 25 (2012).
• Yunan Wu et al. “A comparison of 1-D and 2-D deep convolutional neural networks in ECG classification”. In: arXiv preprint arXiv:1810.07088 (2018).
• Ekaba Bisong. Building machine learning and deep learning models on Google cloud platform. Springer, 2019.