Sakari Junnila

A new method for measuring the ballistocardiogram using EMFi sensors in a normal chair

Ballistocardiography is a non-invasive technique for the assessment of cardiac function. We built a measurement setup to measure the ballistocardiogram from a normal chair using EMFi sensors. The ballistocardiogram is recorded from a subject sitting on the chair. The measured signal is amplified by a specially-designed charge amplifier and digitized by a circulation monitor. A PC provides a user interface for the measurement devices, records the data and displays the results. Impedancecardiography and ECG serve as reference measurements for the ballistocardiography. To test the system, one healthy 24-year-old male and one healthy 22-year-old female were measured. It is concluded that the ballistocardiogram waveforms described in the literature can be recognized from the EMFi signal measured from a normal chair.

Wireless, Multipurpose In-Home Health Monitoring Platform: Two Case Trials

We propose a general purpose home area sensor network and monitoring platform that is intended for e-Health applications, ranging from elderly monitoring to early homecoming after a hospitalization period. Our monitoring platform is multipurpose, meaning that the system is easily configurable for various user needs and is easy to set up. The system could be temporarily rented from a service company by, for example, hospitals, elderly service providers, specialized physiological rehabilitation centers, or individuals. Our system consists of a chosen set of sensors, a wireless sensor network, a home client, and a distant server. We evaluated our concept in two initial trials: one with an elderly woman living in sheltered housing, and the other with a hip surgery patient during his rehabilitation phase. The results prove the functionality of the platform. However, efficient utilization of such platforms requires further work on the actual e-Health service concepts.

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

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

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

UUTE Home Network for Wireless Health Monitoring

This paper presents a home sensor network for wireless health monitoring, including a wireless sensor network, client for controlling the sensor network, and a data storage server. A common software and hardware microcontroller-sensor interface was defined to enable joint use of sensor technologies developed in three different projects. IEEE 802.15.4 RF-transceiver based radio-boards and ZigBee network software were designed and built, along with a simple sensor network software on top of the ZigBee stack, to implement the wireless sensor network. Both commercial and custom made sensors have been interfaced to the sensor network. A set-up consisting of four sensors was developed and tested in a real home environment. The architectural overview of the system and main technical design choices are presented.

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.

Ballistocardiogram classification using a novel transform so-called AliMap and biorthogonal wavelets

This paper presents a new kind of mapping so-called AliMap for signal processing. This map tries to eliminate redundant information without losing relevant information incorporated in spatial, time, and frequency domains. i.e., this map is able to extract most important information and quantify thorn using scalar values, mapping from a high dimensional space to one scalar value. It has three factors to decide which information of input data is most important. This transform can be used for automatic pattern classification. In this study, we applied Haar wavelet transform to extract essential features of the ballistocardiogram (BCG) signal and AliMap to classify the BCC. Initial tests with BCG from 18 subjects (both healthy and unhealthy people) indicate that the method can classify the subjects into three classes with a high accuracy, compared with the well-known method called Starr classification of BCG. The method is insensitive to latency and non-linear disturbance. Moreover, the applied wavelet transform requires no prior knowledge of the statistical distribution of data samples.