Mikko Pänkäälä

Accelerometer-Based method for extracting respiratory and cardiac gating information for dual gating during nuclear medicine imaging

Both respiratory and cardiac motions reduce the quality and consistency of medical imaging specifically in nuclear medicine imaging. Motion artifacts can be eliminated by gating the image acquisition based on the respiratory phase and cardiac contractions throughout the medical imaging procedure. Electrocardiography (ECG), 3-axis accelerometer, and respiration belt data were processed and analyzed from ten healthy volunteers. Seismocardiography (SCG) is a noninvasive accelerometer-based method that measures accelerations caused by respiration and myocardial movements. This study was conducted to investigate the feasibility of the accelerometer-based method in dual gating technique. The SCG provides accelerometer-derived respiratory (ADR) data and accurate information about quiescent phases within the cardiac cycle. The correct information about the status of ventricles and atria helps us to create an improved estimate for quiescent phases within a cardiac cycle. The correlation of ADR signals with the reference respiration belt was investigated using Pearson correlation. High linear correlation was observed between accelerometer-based measurement and reference measurement methods (ECG and Respiration belt). Above all, due to the simplicity of the proposed method, the technique has high potential to be applied in dual gating in clinical cardiac positron emission tomography (PET) to obtain motion-free images in the future.

Current source calibration by combination selection of minimum sized devices

In this paper, we present an effective mismatch compensation method for analog current mode processing. Such a method is required in current and future mixed-mode processing systems, which take advantage of analog processing in addition to conventional digital logic. The proposed design utilizes analog processing devices that can be scaled down with the manufacturing process, therefore employing the advantages of CMOS technology in the form of a smaller implementation area. The range of input currents employed is 1 muA to 10 muA. The example circuit can be calibrated within 1 % of the nominal value at the 4sigma confidence interval with 65 mum2 of implementation area in 0.15mum CMOS technology

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 (ru2009u2009>u2009u20090.99) 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.

Seismocardiography: Toward heart rate variability (HRV) estimation

Heart rate variability (HRV), the variation in the beat-to-beat heart rate, is a key indicator of the cardiovascular condition of an individual. The purpose of this study was to cross-validate the beat-by-beat time variations in seismocardiography (SCG) with electrocardiography (ECG) for determining ultra-short term HRV indices. Twenty healthy young volunteers were examined in this study by performing an ultra-short term data acquisition protocol. Kubios HRV software was utilized to assess the HRV parameters. The HRV indices were analyzed in both time-domain and frequency-domain processes. High linear relationship (r>0.98) and agreement was observed between the HRV indexes calculated from SCG and ECG data. In conclusion, SCG and ECG HRV indices were found to be statistically close enough to warrant the use of SCG for estimating HRV.

Gyrocardiography: A new non-invasive approach in the study of mechanical motions of the heart. Concept, method and initial observations

The pumping action of the heart is performed by contraction of the myocardium fibers. We present a non-invasive technique named gyrocardiography (GCG) that comprises a sensor of angular motion, gyroscope, configured to obtain three-dimensional angular velocity signals. A tri-axial micro electromechanical (MEMS) gyroscope sensor was attached to the surface of the chest to obtain gyrocardiogram. Color-coded Doppler tissue imaging (DTI) was recorded simultaneously and synchronized with the GCG in an off-line analysis. By placing a region of interest longitudinally around the myocardium in DTI allowed us to investigate whether GCG can provide information indicative of the tissue velocity and relative strain rate of the myocardium. Experimental observations by simultaneously recorded GCG and color DTI suggests that a gyroscope sensor attached to the chest is indeed capable to monitor the myocardial deformation during the cardiac cycle and therefore can provide a gateway to clinically relevant information.

A new algorithm for segmentation of cardiac quiescent phases and cardiac time intervals using seismocardiography

Systolic time intervals (STI) have significant diagnostic values for a clinical assessment of the left ventricle in adults. This study was conducted to explore the feasibility of using seismocardiography (SCG) to measure the systolic timings of the cardiac cycle accurately. An algorithm was developed for the automatic localization of the cardiac events (e.g. the opening and closing moments of the aortic and mitral valves). Synchronously acquired SCG and electrocardiography (ECG) enabled an accurate beat to beat estimation of the electromechanical systole (QS2), pre-ejection period (PEP) index and left ventricular ejection time (LVET) index. The performance of the algorithm was evaluated on a healthy test group with no evidence of cardiovascular disease (CVD). STI values were corrected based on Weissler’s regression method in order to assess the correlation between the heart rate and STIs. One can see from the results that STIs correlate poorly with the heart rate (HR) on this test group. An algorithm was developed to visualize the quiescent phases of the cardiac cycle. A color map displaying the magnitude of SCG accelerations for multiple heartbeats visualizes the average cardiac motions and thereby helps to identify quiescent phases. High correlation between the heart rate and the duration of the cardiac quiescent phases was observed.

A gray-code current-mode ADC for mixed-mode cellular computer

Analog to digital converters are used in extremely many applications to convert real world signals into digital words. The converter presented in this paper, is designed for a cellular nonlinear network type system, where A/D converters are included in each cell to transform the gray scale value to be stored in a 6 bit SRAM memory bank. Because of this, the converter structure should have small silicon area, low power consumption and easy controlling. Presented ADC fulfills these requirements.

Compact floating-gate learning array with STDP

In this paper, we present a Spiking Neural Network (SNN) architecture that incorporates Integrate-and-fire (IF) type neurons and floating-gate transistors (FGTs) to store the synaptic weights. Compactness of the network has been the major target throughout the design. We believe that a CrossNet architecture lends itself very well to satisfy this goal. The synaptic weights are updated locally according to an approximation of Spike-Timing-Dependent Plasticity (STDP) rule. While the computations are performed internally in the analog domain the network is interfaced with a digital Address-Event-Representation (AER) to achieve robust off-chip communication. The operation of the array is described and selected simulations with 65nm CMOS are shown.

An Analog 2-D DCT Processor

This paper presents a simple and low-cost analog architecture for computing 2-D discrete cosine transform (2-D DCT). The proposed circuit is aimed for low-power or very high-speed moderate image quality video compression applications. The design uses current-mode signaling and has two separate 1-D DCT kernels, thus no memory is needed for storing intermediate results. Moreover, the circuit works in continuous time. Simple current mirrors have been used to realize all the needed matrix operations and the transistors are dimensioned in such a way that current level of 20 muA is not exceeded to ensure low-power operation. A prototype chip which includes both 4-point and 8-point forward transforms has been fabricated in a 0.18-mum digital CMOS technology. The operation of the circuit is analyzed with help of measurement results obtained from test chips

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.