Alireza Akhbardeh

An EMFi-film sensor based ballistocardiographic chair: performance and cycle extraction method

New sensor technologies open possibilities for measuring traditional biosignals in new innovative ways. This, together with the development of signal processing systems and their computing power, can sometimes give new life to old measurement techniques. Ballistocardiogram is one such technique, originally promising but quickly replaced by the now very popular electrocardiogram. A ballistocardiograph chair, designed to look like a normal office chair, was built and fitted with pressure sensitive EMFi-films. The films are connected via a charge amplifier to a medical bioamplifier. The system was accepted for medical use in Tampere University Hospital and patient measurements have been performed. The system is presented and its performance evaluated. A wireless version of the system is needed to hide the cabling from the user. This makes the chair indistinguishable from a normal office chair. Overview of first wireless prototype is given. To analyze recorded BCG, individual BCG cycles must be extracted from the signal containing respiration and movement artifacts. A method for this and results of its application are presented. The developed system can be used for BCG measurements and it is able to automatically extract individual BCG cycles, but it has some limitations which are presented in the paper.

An Electromechanical Film Sensor Based Wireless Ballistocardiographic Chair: Implementation and Performance

New sensor technologies open possibilities for measuring traditional biosignals in new innovative ways. This, together with the development of signal processing systems and their increasing computing power, can sometimes give new life to old measurement techniques. Ballistocardiogram (BCG) is one such technique, originally promising but later replaced by the now very popular electrocardiogram. It’s usability was previously limited by the large size of the devices required to record it, and the complex nature of the recorded signal, which gave little information in visual inspection. In this paper, we present how a lightweight and flexible electromechanical film (EMFi) sensor can be used to record BCG. A ballistocardiographic chair, designed to look like a normal office chair, was built and fitted with two sensitive EMFi sensors. Two different measurement setups to record the signal from the EMFi sensors were developed. The first, so-called wired setup, uses a commercial bio-amplifier, and a special pre-amplifier to interface to it. The latter, so-called wireless setup, uses our own hardware to transmit the recorded digitized signals wirelessly to a nearby PC. Both of these systems are presented and their performance evaluated. Also, the suitability, limitations and advantages of the EMFi sensor over existing sensors and methods are discussed. The validity of the EMFi sensor and amplifier output is tested using a mechanical vibrator. Lastly, a summary of signal analysis methods developed for our system is given. The developed systems have be used for medical BCG measurements, and the recordings indicate that the both the systems are functional and capture useful BCG signal components.

A wireless ballistocardiographic chair.

This paper presents a wireless ballistocardiographic chair developed for the Proactive Health Monitoring project in the Institute of Signal Processing. EMFi sensors are used for BCG measurement and IEEE 802.15.4 RF link for radio communication between the chair and a PC. The chair measures two BCG signals from the seat and the backrest and a rough ECG signal from the armrests of the chair. The R-spike of the ECG signal can be used as a synchronisation point to extract individual BCG cardiac cycles. Also, two developed methods for extracting BCG cycles without using a reference ECG signal are presented and compared

BSeg++: A modified Blind Segmentation Method for Ballistocardiogram Cycle Extraction

This paper presents a method to extract cardiac cycles and H-I-J components of Ballistocardiogram (BCG). The new improved algorithm BSeg++ permits on the segmentation of BCG signal and extraction of its basic complexes H-I-J without Electrocardiogram (ECG) synchronization. The BSeg++ is based on two previously developed methods described in [1, 2, 3] for extracting BCG cycles without using a reference ECG signal. Those methods suffered from extract redundant BCG cycles because of motion artifacts or BCG fluctuations. In this study, we modified the blind segmentation algorithm and solved its problems. We also added another feature to detect H-I-J complexes of BCG. Also, this new algorithm can be used to extract cardiac cycles and R-S-T components of ECG. The data analysis has been performed on the subjects tested at Simon Fraser University. Initial tests of BCG and ECG from twenty subjects indicate that the method extracted BCG (ECG) cycles and its components with a negligible error in the presence of motion artifacts, BCG fluctuations, latency and non-linear disturbance.

The heart disease diagnosing system based on force sensitive chair's measurement, biorthogonal wavelets and neural networks

The heart disease diagnosing (HDD) system consists of a sensitive movement EMFI-film sensor installed under the upholstery of a chair. Whilst a man rests on the chair, this force sensitive sensor produces a single electrical signal containing components reflective of cardiac-ballistocardiogram (BCG), respiratory, and body movement related motion. Among different measurements of body activities, BCG has an interesting property that no electrodes are needed to be attached to the body during recording. This makes it suitable for evaluation of a mans heart condition in any place such as home, car, or office. This paper describes briefly our developed HDD system and especially a combined intelligent signal processing method to detect, extract features, and finally cluster BCG cycles. The system is designed to assist medical doctors to diagnose heart diseases of subject under test. Indeed, it is a fully automatic system which is not sensitive to any BCG latency as well as non-linear disturbance. It uses high resolution biorthogonal wavelet transform to extract essential BCG features and then clusters them using artificial neural networks (ANNs). Some evaluations using recordings from normal young, normal old and abnormal old volunteers indicated that our combined method is reliable and has a high performance

Comparative analysis of three different modalities for characterization of the seismocardiogram

We introduce and compare three different modalities to study seismocardiogram (SCG) and its correlation with cardiac events. We used an accelerometer attached to the subject sternum to get a reference measure. Cardiac events were then approximately identified using echocardiography. As an alternative approximation, we used consecutive Cine-MRI images of the heart to capture cardiac movements and compared them with the experimental SCG. We also employed an anatomically accurate, finite element base electromechanical model with geometry built completely from DT-MRI to simulate a portion of the cardiac cycle as observed in the SCG signal. The preliminary results demonstrate the usability of these newly proposed methods to investigate the mechanism of SCG waves and also demonstrate the usability of echocardiograph in interpretation of these results in terms of correlating them to underlying cardiac cycle events.

Comparative analysis of seismocardiogram waves with the ultra-low frequency ballistocardiogram

Simultaneous seismocardiogram (SCG) and ultra-low frequency ballistocardiogram (BCG) signals are recorded. Preliminary results from the BCG helped tag which waves on the SCG are related to the rapid systolic ejection and aortic valve closure events. These results agreed with and further confirmed previous findings using the echocardiogram. This is the first reported work on comparisons of SCG and BCG signals and provides a setup to study the effects of arterial circulation on the morphology of the SCG signal.

Evaluation of heart condition based on ballistocardiogram classification using compactly supported wavelet transforms and neural networks

One of the most usual causes of death of the human are among heart diseases. Several electronic devices have been developed to assist clinicians in monitoring and diagnosing heart diseases. Ballistocardiography (BCG) was one of popular methods before the 1970s but after that other methods have replaced it, partly because the devices were difficult to construct. Recently developed sensors offer new unobtrusive possibilities to evaluate the condition of the patients heart even at home without attaching electrodes to the patient. Thus, it is suitable for evaluation of the heart condition in any place because of being user-friendly method. In this study, we applied compactly supported (Daubechies as well as biorthogonal) wavelet transforms in a comparison way to extract essential features of the BCG signal and neural networks to classify the BCG. Initial tests with BCG from six subjects indicate that the method can classify the subjects to three classes with a high accuracy. The method is almost insensitive to latency and non-linear disturbance. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computational complexity and training time are reduced

Towards a heart disease diagnosing system based on force sensitive chair's measurement, biorthogonal wavelets and neural networks

The heart disease diagnosing (HDD) system consists of a sensitive movement EMFi(TM)-film sensor installed under the upholstery of a chair. Whilst a man rests on the chair, this sensor which is sensitive to force gives us a single electrical signal containing components reflective of cardiac-ballistocardiogram (BCG), respiratory, and body movements related motion. Among different measurements of body activities, BCG has the interesting property that no electrodes are needed to be attached to the body during recording, suitable to evaluate man heart condition in any place such as home, car, or his office. This paper describes briefly our developed HDD system and especially a combined intelligent signal processing method to detect, extract features and finally cluster BCG cycles for assisting medical doctors to diagnose heart diseases of person under test. Indeed, it is a fully automatic system which is not very sensitive to the BCG latency as well as non-linear disturbance. It uses high resolution Biorthogonal wavelet transforms to extract essential BCG features and to cluster those using artificial neural networks (ANNs). Some evaluations using recordings from normal young, normal old and abnormal old volunteers indicated that our combined method is reliable and has high performance.

BCG Data Discrimination Using Daubechies Compactly Supported Wavelet Transform and Neural Networks for Heart Disease Diagnosis

Heart diseases are among the one of the most usual causes of death of the mankind. To assist clinicians in monitoring and diagnosing heart diseases, several electronic devices have been developed. One of the methods, ballistocardiography (BCG) was popular before the 1970s but after that other methods have replaced it, partly because the devices were difficult to construct. Recently developed sensors offer new unobtrusive possibilities to evaluate the condition of the patients heart even at home without attaching electrodes to the patient. In this study we applied Daubechies compactly supported wavelet transform to extract essential features of the BCG signal and neural networks to classify the BCG. Initial tests with BCG from six subjects indicate that the method can classify the subjects to three classes with a high accuracy. The method is almost insensitive to latency and non-linear disturbance. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computational complexity and training time are reduced