| 1. |
- Corino, Valentina D.A., et al.
(author)
-
A Mathematical Model of The Atrioventricular Node during Atrial Fibrillation
- 2010
-
In: [Host publication title missing]. - 0276-6574. ; 37, s. 117-120
-
Conference paper (peer-reviewed)abstract
- The atrioventricular (AV) node plays a crucial role during atrial fibrillation (AF). The aim of this study is to present an AV node model which can be fitted to short-term ECG recordings in order to infer certain AV node characteristics. The proposed model is characterized by: i) the arrival rate of atrial impulses; ii) two different refractory periods, corresponding to dual AV nodal paths; iii) the probability of an atrial impulse choosing either of these pathways; iv) a parameter modeling prolongation of the refractory period due to different physiological reasons. The model was tested on atrial fibrillatory ECGs recorded from 33 patients; the average normalized absolute error between the normalized RR histogram and the estimated model probability density function was 0.0023 ± 0.0016, (20-ms bin size, 0–2 s interval). These preliminary results are encouraging as AV nodal properties can be noninvasively assessed by a set of statistical parameters with a simple electrophysiological interpretation.
|
|
| 2. |
- Sandberg, Frida, et al.
(author)
-
Estimation of Respiratory Information from the Built-In Pressure Sensors of a Dialysis Machine
- 2014
-
In: [Host publication title missing]. - 0276-6574. ; 41, s. 853-856
-
Conference paper (peer-reviewed)abstract
- The purpose of the present study is to determine the feasibility of estimating respiratory information from the built-in pressure sensors of a dialysis machine. The study database consists of simultaneous recordings of pressure signals and capnographic signals from 6 patients during 7 hemodialysis treatment sessions. Respiration rates were estimated using respiratory induced variations in the beat- to-beat interval series of the cardiac component of the pressure signal and respiratory induced baseline varia- tions in the pressure signal, respectively. The estimated respiration rates were compared to a reference respira- tion rate determined from the capnograhpic signal. The root-mean-square error of the estimated respiration rate from the baseline variations of the pressure signal was 2.10 breaths/min; the corresponding error of the estimated res- piration rate from the beat-to-beat interval series of the cardiac component was 4.95 breaths/min. The results sug- gest that it is possible to estimate respiratory information from the pressure sensors.
|
|
| 3. |
- Sandberg, Frida, et al.
(author)
-
Model-based analysis of the ventricular response during atrial fibrillation
- 2011
-
In: Computers in Cardiology. - 0276-6574. ; , s. 1-4
-
Conference paper (peer-reviewed)abstract
- We study a model of the atrioventricular node function during atrial fibrillation (AF), for which the model parameters can be estimated from the ECG. The proposed model is defined by parameters which characterize the arrival rate of atrial impulses, the probability of an impulse choosing either one of the two atrioventricular nodal pathways, the refractory periods of these pathways, and the prolongation of the refractory periods. The parameters are estimated from the RR intervals using maximum likelihood estimation, except for the shorter refractory period which is estimated from the RR interval Poincare plot, and the mean arrival rate of atrial impulses by the AF frequency estimated from the ECG. The model was evaluated on 30-min ECG segments from 36 AF patients. The results showed that 88% of the segments can be accurately modeled when the estimated probability density function (PDF) and an empirical PDF were at least 80% in agreement.
|
|
| 4. |
- Stridh, Martin, et al.
(author)
-
Automatic Screening of Atrial Fibrillation in Thumb-ECG Recordings
- 2012
-
In: 2012 Computing in Cardiology (Cinc), Vol 39. - 0276-6574. ; , s. 193-196
-
Conference paper (peer-reviewed)abstract
- The present study proposes a novel sorting algorithm for identification of patients with atrial fibrillation in large one-lead ECG repositories. Repeated measurements at home with automatic transmission of data to a central database is presently tested in the search for atrial fibrillation for the long-term purpose to reduce the incidence of stroke. Such screening rapidly generates large databases of signals waiting to be sorted and prioritized. The one-lead ECGs were first preprocessed to remove baseline wander followed by beat detection and beat classification. A rhythm analysis stage was employed to perform RR interval analysis with negligible influence of ectopic beats and disturbances. RR interval information in combination with a waveform clustering procedure applied to the expected P wave intervals were used to sort the database into a low priority group containing mainly sinus rhythm, a high priority group containing all ECGs with irregular beat patterns, and a third group showing an unreliable RR series. The outcome of the algorithm was compared to an annotated database containing 2837 one-lead ECG recordings from 103 patients where each recording was visually inspected by a physician. The proposed method was able to divide the database into a low-priority group containing 93% (n=2357) of the sinus rhythm cases and a high priority group containing 98% (n=55) of the atrial fibrillation cases. In addition, 3.7% were found to have an unreliable RR series. In conclusion, automatic analysis of one-lead ECG databases can quickly guide the physician to find recordings with high probability to contain atrial fibrillation and can automatically indicate if a recording needs to be remade due to quality problems.
|
|
