Apnea Recognition with Wavelet Neural Networks

ZANIOL, C.; VARRIALE, M.C.; MANICA, E.

doi:10.5540/tema.2018.019.02.0277

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Articles • TEMA (São Carlos) 19 (2) • May-Aug 2018 • https://doi.org/10.5540/tema.2018.019.02.0277 copy

Apnea Recognition with Wavelet Neural Networks

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Apnea is a Sleep Disorder Syndrome characterized by an interruption or reduction of air flow for at least 10 seconds. Polysomnography is a test used to apnea diagnosis. Several signals, including Electrocardiogram (ECG), Electroencephalogram (EEG) and Oxygen Saturation (SpO ₂) are obtained in this diagnostic test. Since most tests for apnea are uncomfortable to the patients, there is an increase search for alternative methods to reduce cost and improve patient well-being. In this work, we use only SpO ₂ data from 25 patients of the St Vincent’s University Hospital, Dublin, to extract parameters connected to a Neural Network to classify patients with apnea or non-apnea. Results confirm that our alternative method can be used as an auxiliary tool for diagnosis by using exclusively SpO ₂ signal.

Keywords:
Neural Network; Sleep Disorder Syndrome; Apnea

Age			BMI			AHI
Mean	Min	Max	Mean	Min	Max	Mean	Min	Max
50	28	68	31.6	25.1	42.5	24.24	2	91

Neurons	05	10	15	20	25	30	35	40	45	50
Sensitivity	33.12	35.32	38.63	37.48	37.92	36.16	37.48	36.75	37.99	38.06
Specificity	94.06	93.90	92.80	92.56	92.76	92.67	92.75	92.66	93.26	91.99
Accuracy	81.55	82.20	82.19	82.62	81.99	82.33	82.44	82.82	82.28	82.28

Neurons	05	10	15	20	25	30	35	40	45	50
Sensitivity	43.82	41.97	42.73	42.68	42.85	40.24	42.65	39.52	42.41	41.45
Specificity	90.98	91.68	91.29	91.45	91.03	92.87	91.89	91.65	91.40	91.87
Accuracy	83.69	83.73	83.72	83.75	83.47	83.62	84.00	83.31	83.66	83.62

Neurons	05	10	15	20	25	30	35	40	45	50
Sensitivity	41.84	43.08	40.77	41.66	42.24	39.24	41.91	42.60	43.10	42.80
Specificity	91.85	90.97	92.19	91.90	91.56	91.92	91.09	91.45	91.06	91.60
Accuracy	83.69	83.68	83.67	83.57	83.49	83.54	83.35	83.52	83.40	83.66

Neurons	05	10	15	20	25	30	35	40	45	50
Sensitivity	0.00	0.01	0.00	0.00	0.00	0.00	0.13	0.00	0.00	4.00
Specificity	99.99	99.97	99.96	99.96	100.00	99.97	99.89	99.96	100.00	95.90
Accuracy	73.90	73.91	73.89	73.88	73.91	73.89	73.91	73.88	73.91	76.89

	Test A		Test B		Test C		Test D
	Min Acc	Max Acc	Min Acc	Max Acc	Min Acc	Max Acc	Min Acc	Max Acc
Neurons	5	40	40	35	35	5	20 and 40	50
Sensitivity	33.12	36.75	39.52	42.65	41.91	41.84	0.00	4.00
Specificity	94.06	92.66	91.65	91.89	91.09	91.85	99.96	95.90
Accuracy	81.55	82.82	83.31	84.00	83.35	83.69	73.88	76.89

	Min Acc¹	Max Acc²	Test D³	de Chazal et al., 2015
Sensitivity	33.12	42.65	4.00	37.50
Specificity	94.06	91.89	95.90	79.00
Accuracy	81.55	84.00	73.88	70.60

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[1] *Corresponding author: Cristina Zaniol - E-mail: criszaniol@gmail.com