Jorge Henriques

Detection of S1 and S2 Heart Sounds by High Frequency Signatures

A new unsupervised and low complexity method for detection of S1 and S2 components of heart sound without the ECG reference is described The most reliable and invariant feature applied in current state-of-the-art of unsupervised heart sound segmentation algorithms is implicitly or explicitly the S1-S2 interval regularity. However; this criterion is inherently prone to noise influence and does not appropriately tackle the heart sound segmentation of arrhythmic cases. A solution based upon a high frequency marker; which is extracted from heart sound using the fast wavelet decomposition, is proposed in order to estimate instantaneous heart rate. This marker is physiologically motivated by the accentuated pressure differences found across heart valves, both in native and prosthetic valves, which leads to distinct high frequency signatures of the valve closing sounds. The algorithm has been validated with heart sound samples collected from patients with mechanical and bio prosthetic heart valve implants in different locations, as well as with patients with native valves. This approach exhibits high sensitivity and specificity without being dependent on the valve type nor their implant position. Further more, it exhibits invariance with respect to normal sinus rhythm (NSR) arrhythmias and sound recording location

Detection of Atrial Fibrillation using model-based ECG analysis

Atrial fibrillation (AF) is an arrhythmia that can lead to several patient risks. This kind of arrhythmia affects mostly elderly people, in particular those who suffer from heart failure (one of the main causes of hospitalization). Thus, detection of AF becomes decisive in the prevention of cardiac threats. In this paper an algorithm for AF detection based on a novel algorithm architecture and feature extraction methods is proposed. The aforementioned architecture is based on the analysis of the three main physiological characteristics of AF: i) P wave absence ii) heart rate irregularity and iii) atrial activity (AA). Discriminative features are extracted using model-based statistic and frequency based approaches. Sensitivity and specificity results (respectively, 93.80% and 96.09% using the MIT-BIH AF database) show that the proposed algorithm is able to outperform state-of-the-art methods.

Outcome Analysis of Intracorneal Ring Segments for the Treatment of Keratoconus Based on Visual, Refractive, and Aberrometric Impairment

PURPOSE To analyze the outcomes of intracorneal ring segment (ICRS) implantation for the treatment of keratoconus based on preoperative visual impairment. DESIGN Multicenter, retrospective, nonrandomized study. METHODS A total of 611 eyes of 361 keratoconic patients were evaluated. Subjects were classified according to their preoperative corrected distance visual acuity (CDVA) into 5 different groups: grade I, CDVA of 0.90 or better; grade II, CDVA equal to or better than 0.60 and worse than 0.90; grade III, CDVA equal to or better than 0.40 and worse than 0.60; grade IV, CDVA equal to or better than 0.20 and worse than 0.40; and grade plus, CDVA worse than 0.20. Success and failure indices were defined based on visual, refractive, corneal topographic, and aberrometric data and evaluated in each group 6 months after ICRS implantation. RESULTS Significant improvement after the procedure was observed regarding uncorrected distance visual acuity in all grades (P < .05). CDVA significantly decreased in grade I (P < .01) but significantly increased in all other grades (P < .05). A total of 37.9% of patients with preoperative CDVA 0.6 or better gained 1 or more lines of CDVA, whereas 82.8% of patients with preoperative CDVA 0.4 or worse gained 1 or more lines of CDVA (P < .01). Spherical equivalent and keratometry readings showed a significant reduction in all grades (P ≤ .02). Corneal higher-order aberrations did not change after the procedure (P ≥ .05). CONCLUSIONS Based on preoperative visual impairment, ICRS implantation provides significantly better results in patients with a severe form of the disease. A notable loss of CDVA lines can be expected in patients with a milder form of keratoconus.

A New Algorithm for Detection of S1 and S2 Heart Sounds

This paper presents a new algorithm for segmentation and classification of S1 and S2 heart sounds without ECG reference. The proposed approach is composed of three main stages. In the first stage the fundamental heart sound lobes are identified using a fast wavelet transform and the Shannon energy. Next, these lobes are validated and classified into S1 and S2 classes based on Mel-frequency coefficients and on a non-supervised neural network. Finally, regular heart cycles are identified in a post-processing stage by a heart rhythm criterion. This approach was tested using sound samples collected from prosthetic valve implanted patients. Results are comparable with ECG based approaches

Noise detection during heart sound recording using periodicity signatures.

Heart sound is a valuable biosignal for diagnosis of a large set of cardiac diseases. Ambient and physiological noise interference is one of the most usual and highly probable incidents during heart sound acquisition. It tends to change the morphological characteristics of heart sound that may carry important information for heart disease diagnosis. In this paper, we propose a new method applicable in real time to detect ambient and internal body noises manifested in heart sound during acquisition. The algorithm is developed on the basis of the periodic nature of heart sounds and physiologically inspired criteria. A small segment of uncontaminated heart sound exhibiting periodicity in time as well as in the time-frequency domain is first detected and applied as a reference signal in discriminating noise from the sound. The proposed technique has been tested with a database of heart sounds collected from 71 subjects with several types of heart disease inducing several noises during recording. The achieved average sensitivity and specificity are 95.88% and 97.56%, respectively.

Low complexity algorithm for heart sound segmentation using the variance fractal dimension

This paper presents an algorithm for S1 and S2 heart sound segmentation using variance fractal dimension. Heart sound is assumed as a non-stationary signal embedding two main sounds S1 arid S2, murmurs and eventually unusual ambient sound. The variance fractal dimension is applied to adaptively identify the boundaries of sound lobes. S1 components are detected using QRS synchronization while for S2 components a non-supervised classification approach is applied, based on temporal features of the lobes. This allows a 2-lead ECG signal to be used for the task. Some preliminary results are presented using recorded heart sounds taken a few days after valve replacement.

Adaptive RBFNN versus conventional self-tuning: comparison of two parametric model approaches for non-linear control

Abstract In this work a practical study evaluates two parametric modelling approaches — linear and non-linear (neural) — for automatic adaptive control. The neural adaptive control is based on a developed hybrid learning technique using an adaptive (on-line) learning rate for a Gaussian radial basis function neural network. The linear approach is used for a self-tuning pole-placement controller. A selective forgetting factor method is applied to both control schemes: in the neural case to estimate on-line the second-layer weights and in the linear case to estimate the parameters of the linear process model. These two techniques are applied to a laboratory-scaled bench plant with the possibility of dynamic changes and different types of disturbances. Experimental results show the superior performance of the neural approach particularly when there are dynamic changes in the process.

Comparison of systolic time interval measurement modalities for portable devices

Systolic time intervals (STI) have shown significant diagnostic and prognostic value to assess the global cardiac function. Their value has been largely established in hospital settings. Currently, STI are considered a promising tool for long-term patient follow-up with chronic cardiovascular diseases. Several technologies exist that enable beat-by-beat assessment of STI in personal health application scenarios. A comparative study is presented using the echocardiographic gold standard synchronized with impedance cardiography (ICG), phonocardiography (PCG) and photoplethysmography (PPG). The ability of these competing technologies in assessing the pre ejection period (PEP) and the left ventricle ejection time (LVET) is given a general overview with comparative results.

Supervision and c-Means clustering of PID controllers for a solar power plant

Abstract A hierarchical control strategy consisting on a supervisory switching of PID controllers, simplified using the c-Means clustering technique, is developed and applied to the distributed collector field of a solar power plant. The main characteristic of this solar plant is that the primary energy source, the solar radiation, cannot be manipulated. It varies throughout the day, causing changes in plant dynamics conducting to distinct several operating points. To guarantee good performances in all operating points, a local PID controller is tuned to each operating point and a supervisory strategy is proposed and applied to switch among these controllers accordingly to the actual measured conditions. Each PID controller has been tuned off-line, by the combination of a dynamic recurrent non-linear neural network model with a pole placement control design. To reduce the number of local controllers, to be selected by the supervisor, a c-Means clustering technique was used. Simulation and experimental results, obtained at Plataforma Solar de Almeria, Spain, are presented showing the effectiveness of the proposed approach.

Third Heart Sound Detection Using Wavelet Transform-Simplicity Filter

Heart failure and heart valvar diseases are chronic heart disorders which are potentially diagnosed using heart sound characteristics. Heart sound components S1 and S2 exhibit significant characteristics for valvar dysfunction while pathological S3 sound is a prominent sign for heart failure in elderly people. In this paper, a new automatic detection method of the S3 heart sound is proposed. The method is build upon wavelet transform-simplicity filter which separates S1, S2 and S3 sounds from background noise enabling heart sound segmentation even in the presence of heart murmurs or noise sources. The algorithm uses physiologically inspired criteria to assess the presence of S3 heart sound components and to perform their segmentation. Heart sound samples recorded from children as well as from elderly patients with heart failure were used to test the method. The achieved sensitivity and specificity were 90.35% and 92.35%, respectively.