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Wang W, Alzate-Correa D, Alves MJ, Jones M, Garcia AJ, Zhao J, Czeisler CM, Otero JJ. Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution. Respir Physiol Neurobiol 2020; 283:103558. [PMID: 33010456 DOI: 10.1016/j.resp.2020.103558] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 09/24/2020] [Accepted: 09/26/2020] [Indexed: 11/16/2022]
Abstract
Respiratory parameters change during post-natal development, but the nature of their changes have not been well-described. The advent of commercially available plethysmographic instruments provided improved repeatability of measurements and standardization of measured breathing in mice across laboratories. These technologies thus allowed for exploration of more precise respiratory pattern changes during the post-natal developmental epoch. Current methods to analyze respiratory behavior utilize plethysmography to acquire standing values of frequency, volume and flow at specific time points in murine maturation. These metrics have historically been independently analyzed as a function of time with no further analysis examining the interplay these variables have with each other and in the context of postnatal maturation or during blood gas homeostasis. We posit that machine learning workflows can provide deeper physiological understanding into the postnatal development of respiration. In this manuscript, we delineate a machine learning workflow based on the R-statistical programming language to examine how variation and relationships of frequency (f) and tidal volume (TV) change with respect to inspiratory and expiratory parameters. Our analytical workflows could successfully predict age and found that the variation and relationships between respiratory metrics are dynamically shifting with age and during hypercapnic breathing. Thus, our work demonstrates the utility of high dimensional analyses to provide reliable class label predictions using non-invasive respiratory metrics. These approaches may be useful in large-scale phenotyping across development and in disease.
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Affiliation(s)
- Wesley Wang
- Department of Pathology, Division of Neuropathology, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Diego Alzate-Correa
- Department of Pathology, Division of Neuropathology, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Michele Joana Alves
- Department of Pathology, Division of Neuropathology, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Mikayla Jones
- Department of Pathology, Division of Neuropathology, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Alfredo J Garcia
- Department of Emergency Medicine, University of Chicago, Chicago, IL, United States
| | - Jing Zhao
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Catherine Miriam Czeisler
- Department of Pathology, Division of Neuropathology, The Ohio State University College of Medicine, Columbus, OH, United States.
| | - José Javier Otero
- Department of Pathology, Division of Neuropathology, The Ohio State University College of Medicine, Columbus, OH, United States.
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Haldar NAH, Khan FA, Ali A, Abbas H. Arrhythmia classification using Mahalanobis distance based improved Fuzzy C-Means clustering for mobile health monitoring systems. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.08.042] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Haider A, Tavakolian K, Fazel-Rezai R. Performance Evaluation of Heartbeat Classification Methods Based on Morphological Descriptors1. J Med Device 2015. [DOI: 10.1115/1.4030120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- Ali Haider
- Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58202
| | - Kouhyar Tavakolian
- Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58202
| | - Reza Fazel-Rezai
- Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58202
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A wavelet transform based feature extraction and classification of cardiac disorder. J Med Syst 2014; 38:98. [PMID: 25023652 DOI: 10.1007/s10916-014-0098-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2013] [Accepted: 07/01/2014] [Indexed: 10/25/2022]
Abstract
This paper approaches an intellectual diagnosis system using hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of Electrocardiogram (ECG) signals. This method is based on using Symlet Wavelet Transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmias. In these particular parameters were used as input of ANFIS classifier, five most important types of ECG signals they are Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Pre-Ventricular Contraction (PVC), Ventricular Fibrillation (VF), and Ventricular Flutter (VFLU) Myocardial Ischemia. The inclusion of ANFIS in the complex investigating algorithms yields very interesting recognition and classification capabilities across a broad spectrum of biomedical engineering. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies. The results give importance to that the proposed ANFIS model illustrates potential advantage in classifying the ECG signals. The classification accuracy of 98.24 % is achieved.
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