Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. Results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89% and 86%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD.
Highlights
Chronic Obstructive Pulmonary Disease (COPD) is an inflammatory lung disease, causing breathing difficulties in patients due to obstructed airflow in lungs[1]
Machine Learning (ML) tools applied on the clinical data acquired from PoC devices enable the efficient management of chronic diseases such as COPD
Deployment of the model with 10-level resolution on the memristive neuromorphic platform has led to an on-chip recognition accuracy of 89%, indicating the reliability of the approach for the management of COPD in real-world applications (Table 3)
Summary
Chronic Obstructive Pulmonary Disease (COPD) is an inflammatory lung disease, causing breathing difficulties in patients due to obstructed airflow in lungs[1]. The accurate diagnosis of the disease based on this approach is only possible by concurrent consideration of various personal–medical parameters related to patients. These parameters include demographic information of patients such age, gender, and the smoking background. The astonishing performance of ML for various studies, shortcomings of cloud-based techniques have limited their real-world applications in medicine[18] These shortcomings include data safety concerns related to securing sensitive medical data in a single database, susceptible to malicious attacks or scandals. Cloud-based techniques require immense energy consumption and enormous computational power, restricting their application for low-power PoC devices[9,19]
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