Classification of Respiration Episodes using. Fuzzy Logic. Maria I. Restrepo, Susmita Bhandari, and Taikang Ning. Department of Engineering, Trinity College.
Classification of Respiration Episodes using Fuzzy Logic Maria I. Restrepo, Susmita Bhandari, and Taikang Ning Department of Engineering, Trinity College 300 Summit Street Hartford, CT 06106 are saved and the ‘history’ of the signal is analyzed, then one can more accurately determine the true state of the respiratory signal. This classificatory method can reduce false alarms and improve signal classification.
Abstract—Respiratory signals collected from young adults using Biopac’s abdominal strain gauge were properly filtered, amplified and digitized. An algorithm that combined Autoregressive (AR) and modified zero-crossing models was used to extract signal parameters such as the energy and frequency of the underlying signal. These parameters were used in a classification scheme based on fuzzy logic. Because of the variability of respiration signals, fuzzy logic provides a more natural classification as opposed to threshold based method [3][4]. Experimental results show that fuzzy logic presents a flexible and adaptable classificatory mechanism, which shows in percentage to which a segment of respiration signal belongs to one of the following categories: normal respiration, respiration with artifacts or apnea. It can be effectively used to reduce false alarms and improve classification of ambiguous cases.
II. METHODS The respiration signal will be divided into non-overlapped 10second segments and each segment will be scored as one of the said categories. Using a sampling frequency of 20 Hz, 200 data samples were collected for each segment. Two parameters are calculated for each segment, namely, respiration energy (ENGY) and rate (RATE). The energy is calculated as the following:
I. INTRODUCTION
ENGY =
Sleeping apnea [1] has been an area open to research because it affects millions of Americans. There are several monitoring systems [2] that can collect and analyze respiratory signals, and ultimately detect the presence of apnea. During previous studies [3]-[4], respiratory signals were collected using an abdominal gauge. The signals were classified either as normal respiration, apnea or respiration with artifacts. An algorithm that combined Autoregressive (AR) and zero crossing models showed to be very effective when detecting apnea and normal respiration. However, there were several cases of respiration with artifacts that were difficult to classify using threshold based approach, where the signal was unclassified or the system detected ‘false apnea episodes.’
N
∑ x ( n)
2
(1)
n =1
where N is the number of total data samples. The respiratory rate was calculated using a modified zero-crossing algorithm with the baseline set to be 75% of the average energy index. Such modification prevents miscalculation due to changes of the mean respiratory level. The rate calculated by zerocrossing is averaged with the rate computed by AR modeling [4]. A fuzzy logic based classification is then designed using ENGY and RATE. The system was constructed using Simulink and is shown in the block diagram in Figure 1.
The AR model and the modified-zero crossing algorithm [3][4] provided a way to identify the energy and frequency of the respiratory signal. In order to classify the signal, appropriate threshold values were used to determine the type of signal. The method proposed in this paper does not classify respiratory signals in an absolute manner. By implementing fuzzy logic [6] as a classificatory method, a signal can be classified as normal respiration to a certain degree and as respiration with artifacts to another.
Engy
[respin]
Signal in Resp. Rate
Signal From Workspace
Respiration Analysis
Rate Transition
Fuzzy Logic Controller with Ruleviewer
Figure 1: Fuzzy Logic Classification System The system shown in Figure 1 was further broken down into individual block diagrams with each block diagram being composed of specific signal processing task.
The advantage of fuzzy logic approach is that it can provide a more natural and reasonable description of a signal that is unnecessarily precise. If the sampled segments from a signal
0-7803-9564-6/06/$20.00 ©2006 IEEE
1 N
133
In fuzzy logic a membership function (MF) is used to define how each given input is mapped to a membership value between 0 and 1 [6]. Without loss of generality, the membership functions used to map the inputs are given by Gaussian distribution, whereas the output is mapped using a triangular membership function. Figure 2 shows the membership function that maps ENGY parameter.
Figure 4: Fuzzy logic classification of a respiration episode with motion artifacts This preliminary result has shown that fuzzy logic can be effectively applied to classify respiration signals by providing additional information regarding the percentage of each target category. The rules used in fuzzy logic inference can be efficiently modified to take into account of some ambiguous respiration episodes. Furthermore, fuzzy logic can be implemented together with threshold based classification to allow score the transition of respiration episodes from one to another category.
Figure 2: Membership function (MF) of ENGY parameter After parameters, ENGY and RATE, were fuzzified through the membership functions, the classification rules could be edited to perform classification. The overall fuzzy inference system is shown in Figure 3.
REFERENCES [1] J. N. McNames and A. M. Fraser, "Obstructive Sleep Apnea Classification based on Spectrogram Patterns in the Electrocardiogram," Computers in Cardiology, Vol 27, pp.749-752, 2000. [2] R. S. Mendenhall and M. R. Neuman, "Efficacy of Five Nininvasive Infant Respiration Sensors," IEEE Frontiers of Engineering and Computing in Health Care, pp.303307, 1983. [3] K. Nepal, E. Biegeleisen and T. Ning, “Apnea detection and respiration rate estimation through parametric modeling,” Proc. IEEE 28th Annual Northeast Bioeng. Conf., pp.277-278, Drexel Univ., Philadelphia, PA, April 20-21, 2002. [4] T. Ning and J. D. Bronzino, "Automatic Classification of Respiratory Signals," Proc. of IEEE/EMBS 11th Annual International Conf., pp.669-670, Seattle, WA. Nov. 1989 [5] S. Reisch , J. Timmer, H. Steltner, K. Rühle ,J. H. Ficker, J. Guttmann, "Detection of obstructive sleep apnea by analysis of phase angle using the forced oscillation signal," Respiration Physiology 123, pp.87-99, 2000. [6] “Fuzzy logic toolbox tutorial,” Mathworks, Inc., February 2006.
Figure 3: Fuzzy logic classification of respiration signals.
III. RESULTS AND CONCLUSIONS The respiratory signals collected from young adults were analyzed. Simulation results obtained are consistent with [3]. In normal respiration and apnea episodes, the fuzzy logic based classification performed as well as threshold based approach [3]-[4]. However, in situations where respiration signals are corrupted with motion artifacts, fuzzy logic classification provides a more realistic inference. Figure 4 shows how a respiration episode which had been previously identified as normal respiration using threshold based classification being classified using fuzzy logic in terms of its percentage in the categories of normal respiration and respiration with motion artifacts.
134
