Vector-Based Analysis of the Similarity Between Breathing and Heart Rate During Paced Deep Breathing
被引:2
作者:
Kleyko, Denis
论文数: 0引用数: 0
h-index: 0
机构:
Lulea Univ Technol, Lulea, SwedenLulea Univ Technol, Lulea, Sweden
Kleyko, Denis
[1
]
论文数: 引用数:
h-index:
机构:
Osipov, Evgeny
[1
]
Wiklund, Urban
论文数: 0引用数: 0
h-index: 0
机构:
Umea Univ, Umea, SwedenLulea Univ Technol, Lulea, Sweden
Wiklund, Urban
[2
]
机构:
[1] Lulea Univ Technol, Lulea, Sweden
[2] Umea Univ, Umea, Sweden
来源:
2018 COMPUTING IN CARDIOLOGY CONFERENCE (CINC)
|
2018年
/
45卷
关键词:
D O I:
10.22489/CinC.2018.146
中图分类号:
TP39 [计算机的应用];
学科分类号:
081203 ;
0835 ;
摘要:
The heart rate (HR) response to paced deep breathing (DB) is a common test of autonomic function, where the scoring is based on indices reflecting the overall heart rate variability (HRV), where high scores are considered as normal findings but can also reflect arrhythmias. This study presents a method based on hyperdimensional computing for assessment of the similarity between feature vectors derived from the HR and breathing signals. The proposed method was used to identify subjects where HR did not follow the paced breathing pattern in recordings from DB tests in 174 healthy subjects and 135 patients with cardiac autonomic neuropathy. Subjects were classified in 4 similarity classes, where the lowest similiarity class included 35 patients and 3 controls. In general, the autonomic function cannot be evaluated in subjects in the lowest similarity class if they also present with high HRV scores, since this combination is a strong indicator of the presence of arrhythmias. Thus, the proposed vector-based similarity analysis is one tool to identify subjects with high HRV but low cardiorespiratory synchronization during the DB test, which falsely can be interpreted as normal autonomic function.