Diaphragm, Neck and Chest Wall Muscles Activity in COPD patients for Non-Invasive Diagnosis using EMG and MMG Signal Analysis

Diaphragm, Neck and Chest Wall Muscles Activity in COPD patients for Non-Invasive
Diagnosis using EMG and MMG
Signal Analysis

Table of Contents
Introduction.................................................................................................................................................3
Critical evaluation:......................................................................................................................................4
Literature Review........................................................................................................................................5
Evaluating tool to understand the better analysis of Copd patient.............................................................5
Adaptive Filter analysis:...............................................................................................................................6
Use of Adaptive Noise Canceller..................................................................................................................7
Discussion...................................................................................................................................................8
Conclusions...............................................................................................................................................11
Reference...................................................................................................................................................12
Appendix:..................................................................................................................................................14

Introduction
Chronic obstructive pulmonary disease (COPD) is fourth foremostreason of death in the world .
Due to ageing of the population and continuation of risk factors, this problem will keep
increasing in the coming decades. The disease is characterized by an airway obstruction leading
to airflow limitation and, as a consequence, persisted respiratory symptoms such as dyspnea and
production of sputum (Estrada et al. 2016). Last-stage COPD patient normally suffers chronic
hypercapnic respiratory failure, which is accompanying with the end-of-life. Long-term
application of nocturnal intermittent non-invasive ventilation (NIV) in sufferer with chronic
hypercapnic respiratory failure due to neuromuscular and thoracic restrictive disorders improves
clinical outcomes and survival. However, this therapy has long been controversial in COPD
patients [3, 4]. High-intensity NIV is well-defined as a mode of ventilation that delivers
sufficient inspiratory progressive airway pressure in arrangement with higher backup breathing
incidence to decrease arterial carbon dioxide levels. However, for some patients, adapting to
high-intensity NIV is difficult and compliance rates in clinical practice are therefore sometimes
disappointing. Furthermore, the response to NIV in context of progress in gas altercation and
patient-centered results such as improvement in healthy quality of life is variable between
patients, despite the application of high-intensity NIV. A reason for a more prolonged adaption
period, lower compliance rates and less effective ventilation might be the occurrence of patient-
ventilator asynchrony with high-intensity NIV. The surface diaphragm electromyography
(EMGdi) indicatordelivers a real-time ancillary measure of neural respiratory drive that shows
the load on respiratory muscles (Stocks and quanjer, 2015). Its non-invasive nature makes the
method extremely useful during NIV; both to visually detect patient-ventilator asynchrony, and
for repeated measurements (Quanjer et al. 2012). However, for longer recordings, such as whole
night recordings, visual inspection is burdensome and time-consuming. In addition, NIV is
normally applied during sleep. Thus, there is a clear need to develop an instinctiveprocess to
reliably detect the respiratory onset from the EMGdi signal. This is crucial since the timing of
respiratory effort versus the timing of the ventilator pressure wave determines the control of
ventilation. Nevertheless, the EMGdi signal is heavily contaminated by the electrocardiographic
(ECG) activity, compromising inspiratory onset detection.

Critical evaluation:
A number of methods have been used to estimate the envelope of EMGdi and automatically
identify inspiratory onset in the journal "Inspiratory muscle activation increases with COPD
severity as confirmed by non-invasive mechanographic" - by Leonardo Sarlabous. Recently,
fixed sample entropy (fSE) has proved to be a more robust technique to estimate the amplitude
variation in respiratory EMGdisignals. In addition, fSE permits extracting the EMGdi envelope
without requiring a prior removal of QRS complexes thus it is identified as the research
relevancy. However, poor EMGdi signal quality or high ECG interference can reduce the
robustness of fSE. The use of adaptive filters has also been proposed to remove ECG
interference and then estimate EMGdi amplitude and has made the research accurate as well as
reliable. However, these methods have been evaluated separately and in different contexts and it
lacks in biasness and timeliness. Therefore, there is a clear need to explore whether reducing
ECG interference by adaptive filtering can improve EMGdi envelope estimation and
consequently respiratory onset detection.
In this study, the article" Normative measurement of inspiratory muscles performance by meams
of diaphragm muscle mechanographic signals in CoPD patients during an incremental load
respiratory test”is aimed to compare the performance of previously proposed methods estimating
EMGdi envelope and respiratory onset, to optimize the automatic detection of the onset of neural
respiratory drive in COPD patients initiated on home NIV this includes the relevance of the test.
First, to estimate EMGdi envelope, the use of fSE in combination with adaptive straining was
compared and explored to the RMS-based EMGdi envelope provided by EMG acquisition device
helps in understanding the realistic nature of the study. Formerly, dynamic threshold based on
the kernel density estimation (KDE), applied to the EMGdi envelopes, and was proposed to
detect the respiratory onset. The performance of the onset detection was validated using EMG-
based visual scores completed by two well-trained clinicians and describes the timeliness and
completeness of the research.