Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review

Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios.info:eu-repo/semantics/publishedVersio

Pervasive Detection of Sleep Apnea using Medical Wireless Sensor Networks

Abstract-The sleep apnea is a sleep disorder characterized by cessation of respiratory flow (apnea) or a reduction in the flow (hypopnea). This disorder is often invalidating and may in some cases lead to death. During the night, symptoms can include nocturnal choking, heavy snoring, sweating, restless sleep, impotence, and witnessed apnea. As the sleep centers for apnea detection are usually overloaded and inaccessible, an automatic apnea detection algorithm for portable devices is required for inhome detection. In this paper, we propose a lightweight approach for pervasive detection of sleep apnea using Wireless Sensor Networks. The experimental results show that our proposed approach achieves good detection accuracy with low delay and low false alarm rate

Pattern recognition applied to airflow recordings to help in sleep Apnea-Hypopnea Syndrome diagnosis

El Síndrome de la Apnea Hipopnea del Sueño (SAHS) es un trastorno caracterizado por pausas respiratorias durante el sueño. Se considera un grave problema de salud que afecta muy negativamente a la calidad de vida y está relacionada con las principales causas de mortalidad, como los accidentes cardiovasculares y cerebrovasculares. A pesar de su elevada prevalencia (2–7%) se considera una enfermedad infradiagnosticada. El diagnóstico estándar se realiza mediante polisomnografía (PSG) nocturna, que es un método complejo y de alto coste. Estas limitaciones han originado largas listas de espera. Esta Tesis Doctoral tiene como principal objetivo simplificar la metodología de diagnóstico del SAHS . Para ello, se propone el análisis exhaustivo de la señal de flujo aéreo monocanal. La metodología propuesta se basa en tres fases (i) extracción de características, (ii) selección de características, y (iii) procesado de la señal mediante métodos de reconocimiento de patrones. Los resultados obtenidos muestran un alto rendimiento diagnóstico de la propuesta tanto en la detección como en la determinación del grado de severidad del SAHS. Por ello, la principal conclusión de la Tesis Doctoral es que los métodos de reconocimiento automático de patrones aplicados sobre la señal de flujo aéreo monocanal resultan de utilidad para reducir la complejidad del proceso de diagnóstico del SAHS.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemátic

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.https://doi.org/10.3390/e1809027

Detection of Obstructive Sleep Apnoea Using Features Extracted From Segmented Time-Series ECG Signals With a One Dimensional Convolutional Neural Network

This paper reports on ongoing research, which aims to prove that features of Obstructed Sleep Apnoea (OSA) can be automatically identified from single-lead electrocardiogram (ECG) signals using a One-Dimensional Convolutional Neural Network (1DCNN) model. The 1DCNN is also compared against other machine learning (ML) classifier models, namely Support Vector Machine (SVM) and Random Forest Classifier (RFC). The 1DCNN architecture consists of 4 major parts, a Convolutional Layer, a Flattened Dense Layer, a Max Pooling Layer and a Fully Connected Multilayer Perceptron (MLP), with 1 Hidden Layer and a SoftMax output. The model repeatedly learns how to better extract prominent features from one-dimensional data and map it to the MLP for increased prediction. Training and validation are achieved using pre-processed time-series ECG signals captured from 35 ECG recordings. Using our unique windowing strategy, the data is shaped into 5 datasets of different window sizes. A total of 15 models (5 for each group, 1DCNNs, RFCs, SVMs) were evaluated using various metrics, with each being run over numerous experiments. Results show the 1DCNN-500 model delivered the greatest degree of accuracy and rapidity in comparison to the best producing RFC and SVM classifiers. 1DCNN-500 (Sensitivity 0.9743, Specificity 0.9708, Accuracy 0.9699); RFC-500 (Sensitivity/Recall (0) 0.90 / (1) 0.94, Precision (0) 0.94 / (1) 0.90, Accuracy 0.91); SVM-500 (Sensitivity (0) 0.94 / (1) 0.50, Precision (0) 0.65 / (1) 0.90, Accuracy 0.72). The model presents a novel approach that could provide support mechanisms in clinical practice to promptly diagnose patients suffering from OSA

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Utilidad de las señales de oximetría y flujo aéreo en el diagnóstico simplificado de la apnea obstructiva del sueño. Diseño de un test automático domiciliario

Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by recurrent episodes of total (apnea) or partial (hypopnea) absence of airflow during sleep. Untreated OSA produces a significant decrease in quality of life and is associated with the main causes of mortality in industrialized countries.However, OSA is considered an underdiagnosed chronic disease. Continuous positive airway pressure (CPAP) is the most common therapeutic option. Nocturnal polysomnography (PSG) in a specialized sleep unit is the reference diagnostic method, although it has low availability and accessibility. Consequently, in recent years there has been a significant demand for abbreviated methods, most of them at home, to reduce waiting lists. The fundamental hypothesis that the use of automatic processing techniques based on machine learning tools could allow maximizing the diagnostic accuracy of a reduced set of combined biomedical signals: overnight oximetry and airflow recorded at patient&#8217;s home. The main objective was to evaluate whether the joint analysis by means of machine learning algorithms of unsupervised SpO2 and AF signals acquired at patient's home leads to a significant increase in diagnostic performance compared to single-channel approaches. A prospective observational study was carried out in which a population referred consecutively to the Sleep Unit showing moderate-to-high clinical suspicion of having OSA was analyzed.All patients underwent an unsupervised PSG at home(gold standard) from which the SpO2 and AF signals were extracted, which were subsequently processed offline.The apnea-hypopnea index(AHI) derived from the PSG was used to confirm or rule out the presence of the disease.Three different approaches for screening patients with suspected OSA were assessed in terms of the source of information used: single-channel based on SpO2, single-channel based on AF, and two-channel combining information from both SpO2 and AF.The automatic processing of the SpO2 and AF signals was developed in 4 stages: preprocessing, feature extraction, feature selection, and pattern recognition. Unsupervised SpO2 and AF recordings were parameterized using the fast correlation-based filter(FCBF)algorithm.The following machine learning methods were used: linear regression(MLR), multilayer perceptron neural networks(MLP) and support vector machines(SVM). The population was divided into independent training and test groups. Agreement between the estimated and the actual AHIderived from at-home PSG was assessed, and typical OSA cutoff points(5, 15, and 30 events/h) were applied. A total of 299 unattended PSGs were performed at home, with a validity percentage of 85.6%. The highest agreement between the estimated AHI and the PSG AHI was reached by the SVMSpO2+AF model, with an CCI 0.93 and a 4-class kappa index 0.71, as well as with an overall accuracy for the 4 OSA severity categories equal to 81.25%, significantly higher than the individual analysis of the SpO2 signal and the airflow signal.The SVMSpO2+AF model achieved the highest diagnostic performance of all algorithms for the detection of severe OSA, with an accuracy of 95.83% and AUC ROC 0.98. In addition, the AUC ROC of the dual-channel models was significantly higher (p<0.01) than that achieved by all the single-channel approaches for the cutoff of 15events/h. The proposed methodology based on the joint automatic analysis of the SpO2 and AF signals acquired at home showed a high complementarity that led to a remarkable increase in diagnostic performance compared to single-channel approaches. The automatic models outperformed the conventional indices(desaturation and airflow-derived indexes) both in terms of correlation and concordance with the AHI from PSG, as well as in terms of overall diagnostic accuracy, providing a moderate increase in diagnostic performance, particularly in the detection of moderate-to-severe OSA.Our findings suggest that the joint analysis of oximetry and airflow signals by means of machine learning methods allows a simplified as well as accurate screening of OSA at patient's home.La Apnea Obstructiva del Sueño (AOS) es un trastorno respiratorio crónico infradiagnosticado caracterizado por la repetición recurrente de episodios de ausencia total (apnea) o parcial (hipopnea) del flujo aéreo (FA) durante el sueño, que disminuye la calidad de vida y aumenta la mortalidad. La CPAP es el tratamiento más habitual, no invasivo, eficaz y coste-efectivo, por lo que favorecer el proceso de diagnóstico es fundamental. La PSG nocturna es el método diagnóstico de referencia, presentando baja disponibilidad y accesibilidad, lo que ha contribuido a desbordar los recursos disponibles, retrasando el diagnóstico y el tratamiento. En contexto de la simplificación diagnóstica portátil, en auge, el uso de únicamente una (monocanal) o dos (bi-canal) señales, como las de SpO2 y FA ha sido ampliamente explorado, aunque la mayoría en entornos hospitalarios controlados. La hipótesis se fundamenta en que las técnicas de procesado automático basadas en machine learning podrían maximizar la precisión diagnóstica de un conjunto reducido de señales combinadas. El objetivo consistió en evaluar si el análisis conjunto mediante algoritmos de aprendizaje automático de las señales de SpO2 y FA no supervisadas adquiridas en el domicilio aumenta el rendimiento diagnóstico en comparación con los enfoques de un solo canal. Se llevó a cabo un estudio observacional prospectivo en pacientes con sospecha moderada-alta de AOS. Se realizó una PSG no supervisada en su domicilio (gold standard de referencia), de la que se extrajeron las señales de SpO2 y FA, procesadas offline posteriormente. El índice de apnea-hipopnea (IAH) derivado de la PSG se empleó para confirmar o descartar la presencia de la enfermedad. Se implementaron y compararon 3 metodologías de screening en función de la fuente de información empleada: (1) monocanal basado en SpO2, (2) monocanal basado en FA, (3) bi-canal combinando SpO2 y FA. El procesado automático de las señales de SpO2 y FA se desarrolló en 4 etapas: preprocesado, extracción de características, selección de características (mediante fast correlation-based filter, FCBF) y reconocimiento de patrones. Cada enfoque de screening se empleó para estimar automáticamente el IAH utilizando los siguientes métodos de machine learning: (1) regresión lineal múltiple (MLR), (2) redes neuronales perceptrón multicapa (MLP) y (3) máquinas vector soporte (SVM). La población se dividió en grupos independientes de entrenamiento (60%) y test (40%). Se realizaron un total de 299 PSGs domiciliarias. Los modelos de enfoque combinado bi-canal alcanzaron valores de concordancia entre el IAH estimado y el IAH de la PSG domiciliaria y de rendimiento diagnóstico para todos los puntos de corte típicos de AOS (5, 15 y 30 e/h) superiores al enfoque monocanal. La mayor concordancia fue alcanzada por el modelo SVMSpO2+FA (CCI 0.93, kappa4 clases 0.71, precisión global 81.25%), significativamente superior a los análisis individuales. El modelo SVMSpO2+FA alcanzó el mayor rendimiento diagnóstico de todos los algoritmos para la detección de AOS grave (precisión 95.83% y AUC ROC 0.98). Además, el AUC ROC de los modelos bi-canal fue superior (p <0.01) al de los enfoques monocanal para el punto de corte de 15 e/h. La metodología propuesta basada en el análisis automático conjunto de las señales de SpO2 y FA adquiridas en el domicilio mostró una alta complementariedad y un notable aumento del rendimiento diagnóstico en comparación con los enfoques monocanal. Los modelos automáticos superaron globalmente a los índices clásicos (de desaturación y de eventos de flujo aéreo), aportando un incremento moderado del rendimiento diagnóstico particularmente en la detección de AOS moderado-grave. Los resultados obtenidos indican que el análisis conjunto de las señales de oximetría y flujo mediante métodos de aprendizaje automático permite un screening simplificado a la vez que preciso de la AOS en el domicilio del paciente.Escuela de DoctoradoDoctorado en Investigación en Ciencias de la Salu

Ventricular Fibrillation detection using time-frequency and the KNN classifier without parameter extraction

[ES] Este trabajo propone la detección de FV y su discriminación de TV y otros ritmos cardiacos basándose en la representación tiempo-frecuencia del ECG y su conversión en imágen como entrada a un clasificador de vecinos más cercanos (KNN) sin necesidad de extracción de parámetros adicionales. Tres variantes de datos de entrada al clasificador son evaluados. Los resultados clasifican la señal en cuatro clases diferentes: ’Normal’ para latidos con ritmo sinusal, ’FV’ para fibrilación ventricular, ’TV’ para taquicardia ventricular y ’Otros’ para el resto de ritmos. Los resultados para detección de FV mostraron 88,27% de sensibilidad y 98,22% de especificidad para la entrada de imágen equivalente reducida que es la más rápida computacionalmente a pesar de obtener resultados de clasificación ligeramente inferiores a las representaciones no reducidas. En el caso de TV, se alcanzó un 88,31% de sensibilidad y 98,80% de especificidad, un 98,14% de sensibilidad y 96,82% de especificidad para ritmo sinusal normal y 96,91% de sensibilidad con 99,06% de especificidad para la clase ’Otros’. Finalmente, se realiza una comparación con otros algoritmos.[EN] This work describes new techniques to improve VF detection and its separation from Ventricular Tachycarida (VT) and other rhythms. It is based on time-frequency representation of the ECG and its use as input in an automatic classifier (K-nearest neighbours - KNN) without any further signal parameter extraction or additional characteristics. For comparison purposes, three time-frequency variants are analysed: pseudo Wigner-Ville representation (RTF), grey-scale image obtained from RTF (IRTF), and reduced image from IRTF (reduced IRTF). Four types of rhythms (classes) are defined: ’Normal’ for sinus rhythm, ’VT’ for ventricular tachycardia, ’VF’ for ventricular fibrillation and ’Others’ for the rest of rhythms. Classification results for VF detection in case of reduced IRTF are 88.27% sensitivity and 98.22% specificity. In case of VT, 88.31% sensitivity and 98.80% specificity is obtained, 98.14% sensitivity and 96.82% specificity for normal rhythms, and 96.91% sensitivity and 99.06% specificity for other rhythms. Finally, results are compared with other authors.Mjahad, A.; Rosado Muñoz, A.; Bataller Mompeán, M.; Francés Víllora, JV.; Guerrero Martínez, JF. (2017). Detección de Fibrilación Ventricular Mediante Tiempo-Frecuencia y Clasificador KNN sin Extracción de Parámetros. Revista Iberoamericana de Automática e Informática industrial. 15(1):124-132. https://doi.org/10.4995/riai.2017.8833OJS124132151Classen, T. A. C. M., Mecklenbrauker, W. F. G., 1980. The Wigner Distribution: A Tool for Time-Frequency Signal Analysis - Part 2: Discrete-Time Signals. Philips Journal of Research 35, 276-350.Cohen, L., Jul 1989a. Time frequency distributions a review. Proceedings of the IEEE 77 (7), 941-981. https://doi.org/10.1109/5.30749Cohen, L., 1989b. Time-frequency distributions-a review. Proceeding of the IEEE 77 (7), 941-981. https://doi.org/10.1109/5.30749Elhaj, F. A., Salim, N., Harris, A. R., Swee, T. T., Ahmed, T., 2016. Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Computer Methods and Programs in Biomedicine 127, 52- 63. https://doi.org/10.1016/j.cmpb.2015.12.024Hlawatsch, F., Boudreaux-Bartels, G. F., April 1992. Linear and quadratic time-frequency signal representations. IEEE Signal Processing Magazine 9 (2), 21-67. https://doi.org/10.1109/79.127284Ibaida, A., Khalil, I., Aug 2010. Distinguishing between Ventricular Tachycardia and Ventricular Fibrillation from Compressed ECG Signal in Wireless body Sensor Networks. In: Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE. pp. 2013-2016. https://doi.org/10.1109/IEMBS.2010.5627888Jekova, I., 2007a. Shock advisory tool: Detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set. Biomedical Signal Processing and Control 2 (1), 25 - 33. https://doi.org/10.1016/j.bspc.2007.01.002Jekova, I., 2007b. Shock advisory tool: Detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set. Biomedical Signal Processing and Control 2 (1), 25-33. https://doi.org/10.1016/j.bspc.2007.01.002Jekova, I., Krasteva, V., 2004. Real time detection of ventricular fibrillation and tachycardia. Physiological measurement 25 (5), 1167. https://doi.org/10.1088/0967-3334/25/5/007Jin, D., Dai, C., Gong, Y., Lu, Y., Zhang, L., Quan, W., Li, Y., 2017. Does the choice of definition for defibrillation and CPR success impact the predictability of ventricular fibrillation waveform analysis? Resuscitation 111, 48 - 54. https://doi.org/10.1016/j.resuscitation.2016.11.022Kabir, M. A., Shahnaz, C., 2012. Denoising of ecg signals based on noise reduction algorithms in emd and wavelet domain. Biomedical Signal Processing and Control l 7 (5). https://doi.org/10.1016/j.bspc.2011.11.003Kao, T.-P., Wang, J.-S., Lin, C.-W., Yang, Y.-T., Juang, F.-C., June 2012. Using bootstrap adaboost with knn for ecg-based automated obstructive sleep apnea detection. In: The 2012 International Joint Conference on Neural Networks (IJCNN). pp. 1-5. https://doi.org/10.1109/IJCNN.2012.6252716Kaur, L., Singh, V., May 2013. Ventricular Fibrillation Detection using Empirical Mode Decomposition and Approximate Entropy. International Journal of Emerging Technology and Advanced Engineering 3 (5), 260-268.Kaur, M., Singh, B., Seema, 2011. Comparison of different approaches for removal of baseline wander from ecg signal. In: Proceedings of the International Conference &Workshop on Emerging Trends in Technology. ICWET'11. ACM, New York, NY, USA, pp. 1290-1294. https://doi.org/10.1145/1980022.1980307Labatut, V., Cherifi, H., May 2011. Accuracy measures for the comparison of classifiers. In: Ali, A.-D. (Ed.), The 5th International Conference on Information Technology. Al-Zaytoonah University of Jordan, amman, Jordan, pp.1,5.Li, Q., Rajagopalan, C., Clifford, G. D., 2014. Ventricular fibrillation and tachycardia classification using a machine learning approach. Biomedical Engineering, IEEE Transactions on 61 (6), 1607-1613.Mahmoud, S. S., Hussain, Z. M., Cosic, I., Fang, Q., 2006. Time-frequency analysis of normal and abnormal biological signals. Biomedical Signal Processing and Control 1 (1), 33 - 43. https://doi.org/10.1016/j.bspc.2006.02.001Martin, W., Flandrin, P., Dec 1985. Wigner-ville spectral analysis of nonstationary processes. IEEE Transactions on Acoustics, Speech, and Signal Processing 33 (6), 1461-1470. https://doi.org/10.1109/TASSP.1985.1164760Mateo, J., Torres, A., Aparicio, A., Santos, J., 2016. An efficient method for ECG beat classification and correction of ectopic beats. Computers and Electrical Engineering 53, 219 - 229. https://doi.org/10.1016/j.compeleceng.2015.12.015Mjahad, A., Rosado-Mu-oz, A., Guerrero-Martinez, J., Bataller-Mompean, M., Frances-Villora, J. V., 2015. ECG Analysis for Ventricular Fibrillation Detection Using a Boltzmann Network. In: Braidot, A., Hadad, A. (Eds.), VI Latin American Congress on Biomedical Engineering CLAIB 2014, Parana, Argentina 29, 30, 31 October 2014. Vol. 49 of IFMBE Proceedings. Springer International Publishing, pp. 532-535. https://doi.org/10.1007/978-3-319-13117-7Murakoshi, N., Aonuma, K., 2013. Epidemiology of arrhythmias and sudden cardiac death in asia. Circulation Journal 77 (10), 2419-2431. https://doi.org/10.1253/circj.CJ-13-1129Othman, M. A., Safri, N. M., Ghani, I. A., Harun, F. K. C., Ariffin, I., 2013. A new semantic mining approach for detecting ventricular tachycardia and ventricular fibrillation. Biomedical Signal Processing and Control 8 (2), 222 - 227.https://doi.org/10.1016/j.bspc.2012.10.001Phong, P. A., Thien, K. Q., Oct 2009. Classification of Cardiac Arrhythmias Using Interval Type-2 TSK Fuzzy System. In: Knowledge and Systems Engineering, 2009. KSE '09. International Conference on. pp. 1-6. https://doi.org/10.1109/KSE.2009.19Poularikas, A. D., 1999. The transforms and applications handbooks. ACRC Handbook published in cooperatio with IEEE Press, Depatment of electrical an computer engineering the univesity of Alabama in Huntsville.Rangayyan, R. M., 2002. Biomedical signal analysis: A case-study approach. In: IEEE Press Series in Biomedical Engineering.Ravindra Pratap Narwaria, S. V., Singhal, P. K., 2011. Removal of baseline wander and power line interference from ecg signal - a survey approach. International Journal of Electronics Engineering 3, 107-111.Rosado, A., Guerrero, J., Bataller, M., Chorro, J., 2001. Fast non-invasive ventricular fibrillation detection method using pseudo wigner-ville distribution. In: Computers in Cardiology 2001. pp. 237-240. https://doi.org/10.1109/CIC.2001.977635Saini, R., Bindal, N., Bansal, P., May 2015. Classification of heart diseases from ecg signals using wavelet transform and knn classifier. In: Computing, Communication Automation (ICCCA), 2015 International Conference on. pp. 1208-1215. https://doi.org/10.1109/CCAA.2015.7148561Sharma, L., Dandapat, S., Mahanta, A., 2010. Ecg signal denoising using higher order statistics in wavelet subbands. Biomedical Signal Processing and Control 5 (3), 214 - 222. https://doi.org/10.1016/j.bspc.2010.03.003Sornmo, L., Laguna, P., 2005. Bioelectrical signal processing in cardiac and neurological applications. In: Elsevier Academic Press.Tan, W., Foo, C. L., Chua, T. W., July 2007. Type-2 Fuzzy System for ECG Arrhythmic Classification. In: Fuzzy Systems Conference, 2007. FUZZIEEE 2007. IEEE International. pp. 1-6. https://doi.org/10.1109/FUZZY.2007.4295478Valenzuela, J. V., 2008. Interpolacion de Formas en Imagenes Usando Morfologia Matematica. Ph.D. thesis, Departamento de Lenguajes, Sistemas Informaticos e Ingeniera de Software Facultad de Informatica Universidad Politecnica de Madrid, Director de Tesis: Jose Crespo del Arco.Viitasalo, M., Karjalainen, J., 1992. Q T intervals at Heart rates From 50 to 120 Beats per Minute During 24 Hour Electrocardiographic Recordings in 100 Healthy Men Effects of Atenolol. American Heart Association 86 (5), 1439-1442. https://doi.org/10.1161/01.CIR.86.5.1439von Borries, R. F., Pierluissi, J. H., Nazeran, H., Jan 2005. Wavelet transformbased ecg baseline drift removal for body surface potential mapping. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. pp. 3891-3894. https://doi.org/10.1109/IEMBS.2005.1615311V.Oppenheim, A., Willsky, A. S., Nawab, S. H., 1998. Signals and systems. Prentice Hall Internationa,Inc, Massachusettes Intitue Technology with Boston Univeristy.Xia, D., Meng, Q., Chen, Y., Zhang, Z., 2014. Classification of Ventricular T achycardia and Fibrillation Based on the Lempel-Ziv Complexity and EMD 8590, 322-329. https://doi.org/10.1007/978-3-319-09330-7_39Xie, H.-B., Zhong-Mei, G., Liu, H., 2011. Classification of Ventricular Tachycardia and Fibrillation Using Fuzzy Similarity-based Approximate entropy. Expert Systems with Applications 38 (4), 3973 - 3981. https://doi.org/10.1016/j.eswa.2010.09.058Yilmaz, B., Arikan, E., Asyali, M. H., April 2010. Use of knn and quadratic discriminant analysis methods for sleep staging from single lead ecg recordings. Biomedical Engineering Meeting (BIYOMUT), 2010 15th National, 1-4. https://doi.org/10.1109/BIYOMUT.2010.5479833Yochum, M., Renaud, C., Jacquir, S., 2016. Automatic detection of p, QRS and t patterns in 12 leads ECG signal based on CWT. Biomedical Signal Processing and Control 25, 46 - 52. https://doi.org/10.1016/j.bspc.2015.10.01

Non-Contact Sleep Monitoring

"The road ahead for preventive medicine seems clear. It is the delivery of high quality, personalised (as opposed to depersonalised) comprehensive medical care to all." Burney, Steiger, and Georges (1964) This world's population is ageing, and this is set to intensify over the next forty years. This demographic shift will result in signicant economic and societal burdens (partic- ularly on healthcare systems). The instantiation of a proactive, preventative approach to delivering healthcare is long recognised, yet is still proving challenging. Recent work has focussed on enabling older adults to age in place in their own homes. This may be realised through the recent technological advancements of aordable healthcare sen- sors and systems which continuously support independent living, particularly through longitudinally monitoring deviations in behavioural and health metrics. Overall health status is contingent on multiple factors including, but not limited to, physical health, mental health, and social and emotional wellbeing; sleep is implicitly linked to each of these factors. This thesis focusses on the investigation and development of an unobtrusive sleep mon- itoring system, particularly suited towards long-term placement in the homes of older adults. The Under Mattress Bed Sensor (UMBS) is an unobstrusive, pressure sensing grid designed to infer bed times and bed exits, and also for the detection of development of bedsores. This work extends the capacity of this sensor. Specically, the novel contri- butions contained within this thesis focus on an in-depth review of the state-of-the-art advances in sleep monitoring, and the development and validation of algorithms which extract and quantify UMBS-derived sleep metrics. Preliminary experimental and community deployments investigated the suitability of the sensor for long-term monitoring. Rigorous experimental development rened algorithms which extract respiration rate as well as motion metrics which outperform traditional forms of ambulatory sleep monitoring. Spatial, temporal, statistical and spatiotemporal features were derived from UMBS data as a means of describing movement during sleep. These features were compared across experimental, domestic and clinical data sets, and across multiple sleeping episodes. Lastly, the optimal classier (built using a combina- tion of the UMBS-derived features) was shown to infer sleep/wake state accurately and reliably across both younger and older cohorts. Through long-term deployment, it is envisaged that the UMBS-derived features (in- cluding spatial, temporal, statistical and spatiotemporal features, respiration rate, and sleep/wake state) may be used to provide unobtrusive, continuous insights into over- all health status, the progression of the symptoms of chronic conditions, and allow the objective measurement of daily (sleep/wake) patterns and routines