Table 4 Comparison of parameters reported in previous studies on the measurement of heartbeats from BCG.

From: Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals

Paper

Study population (size)

Sensor type & location

Algorithm

Acquisition time

Coverage

Performance

Short-term recording

 Mora et al. 19

Healthy

(16)

MEMS accelerometer

attached to a bed slat

J-peak annotation guided by subject-specific search windows

5 min

N/A

IBI MAE:

4.7 ms

 Alvarado-Serrano et al.21

Healthy

(7)

Piezoelectric sensor on a chair

Wavelet transform based J-wave detection

100 s

N/A

IBI LoA:

29 ms

 Brüser et al.22

Healthy

(16)

MEMS accelerometer

on a bed

J-peak detection by feature vector & k-means clustering

26 min

95.9%

IBI MAE:

17 ms

 Pröll et al.23

Hospital patients (14)

Pneumatic pressure sensor embedded in a bed

IJK complex detection by CNN + GRU DL

calculated each 8 s patch

100%

HR RMSE:

2.1 ± 1.1 bpm

 Zhang et al.24

Healthy

(8)

fiber-optics sensor attached to an arm-chair

IJK labeling by biLSTM DL

200–500 s

98.9%

HR RMSE:

1.4 bpm

 Present study

Healthy

(18)

Piezoelectric sensor placed under a bed sheets

ECG wave extraction by biLSTM DL

2 min

100%

IBI MAE:

34 ms

Overnight recording

 Paalasmaaet al.9

Healthy

(46)

Piezoelectric sensor installed under a bed sheet

Adaptive heartbeat shape modeling

N/A

54.1%

IBI MAE:

13 ms

 Siyahjani et al.30

Healthy (45)

Pressure sensor embedded in a bed

Peak detection of bandpass filtered signal

459 min

N/A

30 s epoch HR LoA:

6 bpm

 Zink et al.31

suspected SDB patients (21)

charged polymer foil placed under a bed sheet

Amplitude pattern tracking with quality index

TMT for all subjects: 93 h

65%

IBI MAE: 4 ± 72 ms

 Schranz et al.25

Healthy (11)

3D-accelerometer embedded in a bed

J-peak detection by CNN + ResNet DL

TMT for all subjects: 134 h

N/A

IBI MAE: 27.9 ± 7 ms

 Present study

Healthy (12)

Piezoelectric sensor placed under a bed sheet

ECG wave extraction by biLSTM DL

437 ± 36 min

78.3%

IBI MAE: 46 ± 10 ms

  1. SDB: Sleep disordered breathing; MEMS: Micro Electro Mechanical Systems; HR: heart rate; IBI: inter-beat interval; bpm: beats/min; DL: deep learning; CNN: convolutional neural network; GRU: gate recurrent unit; biLSTM: bidirectional long short-term memory; ResNet: residual neural network; TMT: total measurement time; LoA: limit of agreement; MAE: mean absolute error; RMSE: root mean squared error; N/A: not available.