Improved ECG watermarking technique using curvelet transform

Hiding data in electrocardiogram signals are a big challenge due to the embedded information that can hamper the accuracy of disease detection. On the other hand, hiding data into ECG signals provides more security for, and authenticity of, the patient\u27s data. Some recent studies used non-blind watermarking techniques to embed patient information and data of a patient into ECG signals. However, these techniques are not robust against attacks with noise and show a low performance in terms of parameters such as peak signal to noise ratio (PSNR), normalized correlation (NC), mean square error (MSE), percentage residual difference (PRD), bit error rate (BER), structure similarity index measure (SSIM). In this study, an improved blind ECG-watermarking technique is proposed to embed the information of the patient\u27s data into the ECG signals using curvelet transform. The Euclidean distance between every two curvelet coefficients was computed to cluster the curvelet coefficients and after this, data were embedded into the selected clusters. This was an improvement not only in terms of extracting a hidden message from the watermarked ECG signals, but also robust against image-processing attacks. Performance metrics of SSIM, NC, PSNR and BER were used to measure the superiority of presented work. KL divergence and PRD were also used to reveal data hiding in curvelet coefficients of ECG without disturbing the original signal. The simulation results also demonstrated that the clustering method in the curvelet domain provided the best performance-even when the hidden messages were large size

A Biomagnetic Field Mapping System for Detection of Heart Disease in a Clinical Environment

This PhD was inspired by the clinical demand for a system to triage chest pain and the untapped diagnostic potential of magnetocardiography (MCG) to reliably detect silent ischaemic heart disease, which is responsible for the highest mortality rate of any single disease category. The aim was to develop a low cost and portable biomagnetic field mapping system capable of differentiating between healthy and diseased hearts within an unshielded hospital environment. This entailed the development of a system based on an array of magnetometers with sufficient sensitivity (104fT/ Hz at 10Hz) and noise rejection performance (68.4 ± 3.9 dB) to measure the small magnetic field associated with the heart beat, the magneocardiogram, within a much larger background noise. The array of induction coil magnetometers (ICM) we developed had sufficient sensitivity and were robust to high amplitude noise. These sensors were also cheap to manufacture and capable of operating on battery power, allowing a low cost, portable device to be developed. The key element that allowed us to achieve unshielded operation was the development of an algorithmic spatial filter, used as a substitute to operation within a magnetically shielded room. This coherent noise rejection (CNR) algorithm exploits the difference in spatial coherence between the local cardiac signals and the distant background noise sources. The observed coherence width during a clinical trial of the system within a hospital ward was 2.8 ± 0.9 × 10 6 mm 2 . This allowed us to capture MCG signals with a signal to noise ratio of SNR QRS = 0.93 ± 4.43dB. The performance of CNR was found to improve by 9dB per order of magnitude increase in environmental spatial coherence width. The coherence width can be increased by changes to hospital architecture, electromagnetic field regulation and device design optimisation. The thesis also explores a variety of approaches to obtain binary diagnostic information from MCG, from traditional statistical learning on manually engineered features to machine learning. I found that machine learning techniques, in particular convolutional neural networks (CNN), were able to capture more diagnostic information than traditional techniques and achieved world class prediction accuracy of 88% on the clinical trial dataset

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

Algorithms and systems for home telemonitoring in biomedical applications

During the past decades, the interest of the healthcare community shifted from the simple treatment of the diseases towards the prevention and maintenance of a healthy lifestyle. This approach is associated to a reduced cost for the Health Systems, having to face the constantly increased expenditures due to the reduced mortality for chronical diseases and to the progressive population ageing. Nevertheless, the high costs related to hospitalization of patients for monitoring procedures that could be better performed at home hamper the full implementation of this approach in a traditional way. Information and Communication Technology can provide a solution to implement a care model closer to the patient, crossing the physical boundaries of the hospitals and thus allowing to reach also those patients that, for a geographical or social condition, could not access the health services as other luckier subjects. This is the case of telemonitoring systems, whose aim is that of providing monitoring services for some health-related parameters at a distance, by means of custom-designed electronic devices. In this thesis, the specific issues associated to two telemonitoring applications are presented, along with the proposed solutions and the achieved results. The first telemonitoring application considered is the fetal electrocardiography. Non-invasive fetal electrocardiography is the recording of the fetal heart electrical activity using electrodes placed on the maternal abdomen. It can provide important diagnostic parameters, such as the beat-to-beat heart rate variability, whose recurring analysis would be useful in assessing and monitoring fetal health during pregnancy. Long term electrocardiographic monitoring is sustained by the absence of any collateral effects for both the mother and the fetus. This application has been tackled from several perspectives, mainly acquisition and processing. From the acquisition viewpoint a study on different skin treatments, disposable commercial electrodes and textile electrodes has been performed with the aim of improving the signal acquisition quality, while simplifying the measurement setup. From the processing viewpoint, different algorithms have been developed to allow extracting the fetal ECG heart rate, starting from an on-line ICA algorithm or exploiting a subtractive approach to work on recordings acquired with a reduced number of electrodes. The latter, took part to the international "Physionet/Computing in Cardiology Challenge" in 2013 entering into the top ten best-performing open-source algorithms. The improved version of this algorithm is also presented, which would mark the 5th and 4th position in the final ranking related to the fetal heart rate and fetal RR interval measurements performance, reserved to the open-source challenge entries, taking into account both official and unofficial entrants. The research in this field has been carried out in collaboration with the Pediatric Cardiology Unit of the Hospital G. Brotzu in Cagliari, for the acquisition of non-invasive fetal ECG signals from pregnant voluntary patients. The second telemonitoring application considered is the telerehabilitation of the hand. The execution of rehabilitation exercises has been proven to be effective in recovering hand functionality in a wide variety of invalidating diseases, but the lack of standardization and continuous medical control cause the patients neglecting this therapeutic procedures. Telemonitoring the rehabilitation sessions would allow the physician to closely follow the patients' progresses and compliance to the prescribed adapted exercises. This application leads to the development of a sensorized telerehabilitation system for the execution and objective monitoring of therapeutic exercises at the patients' home and of the telemedicine infrastructure that give the physician the opportunity to monitor patients' progresses through parameters summarizing the patients' performance. The proposed non-CE marked medical device, patent pending, underwent a clinical trial, reviewed and approved by the Italian Public Health Department, involving 20 patients with Rheumatoid Arthritis and 20 with Systemic Sclerosis randomly assigned to the experimental or the control arm, enrolled for 12 weeks in a home rehabilitation program. The trial, carried out with the collaboration of the Rheumatology Department of the Policlinico Universitario of Cagliari, revealed promising results in terms of hand functionality recovering, highlighting greater improvements for the patients enrolled in the experimental arm, that use the proposed telerehabilitation system, with respect to those of the control arm, which perform similar rehabilitation exercises using common objects

Pressure & flow relationship in the pulmonary circulation in man

Background: Current gold standard pulmonary artery pressure measurements (PAP) are not accurate using a fluid filled catheter. High fidelity micromanometer tipped catheters are more accurate both at rest and during exercise. They have been used to examine the pattern of pulmonary artery pressure waveforms under various physiological conditions. A new thoracic impedance device has been developed, the Physioflow 1 ((c)Manatee, France), which has been shown to measure cardiac output (CO) accurately in a variety of respiratory conditions. It is known that PAP varies with changes in posture, sleep and exercise, and may sometimes appear normal at rest in early disease. Resting pressures vary daily. However, pressure and flow is linearly related in physiological ranges and the relationship is relatively constant. Changes in pressure-flow relationships may be missed at rest. It has been shown that response to a given treatment may be missed if measurements are based solely on resting PAP. For this reason the slopes of pressure-flow plots, are more useful than spot measures of pressure and flow. This has not adequately been explored in humans