Tero Hurnanen

A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms.

Heart rate monitoring helps in assessing the functionality and condition of the cardiovascular system. We present a new real-time applicable approach for estimating beat-to-beat time intervals and heart rate in seismocardiograms acquired from a tri-axial microelectromechanical accelerometer. Seismocardiography (SCG) is a non-invasive method for heart monitoring which measures the mechanical activity of the heart. Measuring true beat-to-beat time intervals from SCG could be used for monitoring of the heart rhythm, for heart rate variability analysis and for many other clinical applications. In this paper we present the Hilbert adaptive beat identification technique for the detection of heartbeat timings and inter-beat time intervals in SCG from healthy volunteers in three different positions, i.e. supine, left and right recumbent. Our method is electrocardiogram (ECG) independent, as it does not require any ECG fiducial points to estimate the beat-to-beat intervals. The performance of the algorithm was tested against standard ECG measurements. The average true positive rate, positive prediction value and detection error rate for the different positions were, respectively, supine (95.8%, 96.0% and ≃0.6%), left (99.3%, 98.8% and ≃0.001%) and right (99.53%, 99.3% and ≃0.01%). High correlation and agreement was observed between SCG and ECG inter-beat intervals (r  >  0.99) for all positions, which highlights the capability of the algorithm for SCG heart monitoring from different positions. Additionally, we demonstrate the applicability of the proposed method in smartphone based SCG. In conclusion, the proposed algorithm can be used for real-time continuous unobtrusive cardiac monitoring, smartphone cardiography, and in wearable devices aimed at health and well-being applications.

Automatic detection of atrial fibrillation using MEMS accelerometer

The aim of the study was to assess the applicability of seismocardiogram (SCG) for the detection of atrial fibrillation (AF) in telemonitoring applications. SCG data used in this study consists of simultaneous SCG and ECG recordings of 12 patients during both AF and sinus rhythm (after cardioversion). An SCG-based AF-detection algorithm was developed and its performance tested with the acquired clinical data. The algorithm is able to distinguish AF positive samples from samples with sinus rhythm with high accuracy.

Beat-by-Beat Quantification of Cardiac Cycle Events Detected From Three-Dimensional Precordial Acceleration Signals

The vibrations produced by the cardiovascular system that are coupled to the precordium can be noninvasively detected using accelerometers. This technique is called seismocardiography. Although clinical applications have been proposed for seismocardiography, the physiology underlying the signal is still not clear. The relationship of seismocardiograms of on the back-to-front axis and cardiac events is fairly well known. However, the 3-D seismocardiograms detectable with modern accelerometers have not been quantified in terms of cardiac cycle events. A major reason for this might be the degree of intersubject variability observed in 3-D seismocardiograms. We present a method to quantify 3-D seismocardiography in terms of cardiac cycle events. First, cardiac cycle events are identified from the seismocardiograms, and then, assigned a number based on the location in which the corresponding event was found. 396 cardiac cycle events from 9 healthy subjects and 120 cardiac cycle events from patients suffering from atrial flutter were analyzed. Despite the weak intersubject correlation of the waveforms (0.05, 0.27, and 0.15 for the x-, y-, and z-axes, respectively), the present method managed to find latent similarities in the seismocardiograms of healthy subjects. We observed that in healthy subjects the distribution of cardiac cycle event coordinates was centered on specific locations. These locations were different in patients with atrial flutter. The results suggest that spatial distribution of seismocardiographic cardiac cycle events might be used to discriminate healthy individuals and those with a failing heart.

A smartphone-only solution for detecting indications of acute myocardial infarction

In this paper we consider the detection of indications of acute myocardial infarction (AMI) through a smartphone only solution. AMI is a serious heart condition where a blood vessel of the heart is fully or partially blocked e.g. by a rupture of an atherosclerotic plaque, the arrival of oxygen to the heart muscle is disturbed, and part of the heart muscle tissue dies (irreversible injury) due to insufficient oxygen supply. When a person feels obscure acute chest pain (angina pectoris), it may be caused, for instance, by heartburn or it may be a symptom of AMI. The goal of this paper is to develop a solution, which could either be integrated into an emergency App for the use of telemedicine by trained medical personnel or as a standalone solution to smartphone users in order to help recognizing this life-threatening condition earlier. The developed solution extracts the heart signal of a patient who lies in supine position by utilizing the built-in accelerometer and gyroscope within a smart device (e.g. a smartphone), which is placed on the chest of the patient. The solution does not require any external sensors for the smartphone to operate, but in the future it could be supplemented with ECG, for instance, to improve its performance. We have collected data with smartphone running Google Android from 17 AMI patients before and after percutaneous coronary intervention (PCI), and in addition, control recordings were performed in 23 healthy individuals (CG) and in 12 patients with stable coronary artery disease (CAD) before elective PCI.

Mobile Phone Detection of Atrial Fibrillation With Mechanocardiography: The MODE-AF Study (Mobile Phone Detection of Atrial Fibrillation)

Because of the frequent asymptomatic presentation of atrial fibrillation (AF), stroke is too often its first manifestation.1 For effective stroke prevention, timely diagnosis of AF is crucial. Mobile devices are becoming ubiquitous, providing significant possibilities for screening applications. In mechanocardiography, mechanical cardiac activity is recorded with accelerometers and gyroscopes, standard components of modern smartphones.2 In our previous proof-of-concept study, smartphone mechanocardiography demonstrated 94% sensitivity and 100% specificity for detecting AF among 39 subjects.2 Here, we validate smartphone mechanocardiography detection of AF against visual interpretation of telemetry electrocardiographic recordings in hospitalized patients.

Heartbeat Detection Using Multidimensional Cardiac Motion Signals and Dynamic Balancing

Ballistocardiography (BCG) is seeing a new renaissance mainly due to access of new miniaturized and sensitive MEMS accelometers and gyroscopes that provides us a new tool for unobstrusive measurement of cardiac signals. These signal, however, suffer from high signal morphology variability and commonly signals are at least partly of low quality. A characteristic of a BCG signal is commonly a brief oscillation associated with each heartbeat which caused by the hearts mechanical movement. We developed an algorithm to detect these wavelets using an envelope enhancement filtering and subsequent dynamic balancing to alleviate the problem of high peak amplitude variability. The beat detection resulted in 0.87 % missed beats and 0.31 % false beats using the gyroY axis of the mobile phone’s integrated motion sensors. Also it is shown, that if the used axis could be chosen optimally for each measurement accuracy of 0.22 % missed beats and 0.21 % false beats could be reached within the used measurements. A photoplethysmography (PPG) signal was used as a verification reference. The data set consisted 2 min recordings from 66 healthy subjects and in total 8870 beats.

Adaptive algorithm and parameter optimization for distributed beamforming in OFDM systems

In this paper, we propose and analyse an adaptive algorithm for distributed beamforming in OFDM systems over time and frequency selective channel conditions. We discuss how the key parameters related to convergence speed of the algorithm can be optimized to suit networks with different degrees of transmitter mobility. We show that the proposed adaptive algorithm is beneficial when used in mobile conditions; this is demonstrated by simulation studies using a realistic geometry-based channel model with various transmitter mobilities.

Distributed beamforming for inter-cluster communication in ad hoc networks

Communication between clusters of wireless terminals in an ad hoc network can be initialized using distributed beamforming, where low-power wireless terminals cooperatively form a multiantenna array. Various methods for implementing distributed beamforming in wireless ad hoc networks have been considered before, but little research has been conducted so far in the connection initialization and cluster identification necessary for realizing such communication in practice. We address these issues with the aim of facilitating communication between terminals in separate clusters too far apart for direct signaling. The proposed approach is based on assigning a specific identification code for each cluster, and utilizing this with distributed beamforming to establish links between clusters. This approach can be used also for sharing the spectrum within a limited geographical area between several clusters of wireless terminals.

Precoding for improved performance of Welch-bound signature sets

Welch-bound signature sets are optimal for oversaturated synchronous CDMA in terms of minimizing the multiple access interference. In this paper, precoding of the sum signal of Welch-bound signatures, in order to maximize the mean signal-to-interference ratio for conventional receivers, is analysed. The gain compared to normal sum signal is calculated, and also the effect on the energy of the sum signal is derived.