Diagnosis of sleep apnea
Methods and apparatuses for detecting sleep apnea by analyzing characteristic physiological oscillations of the heart rate variability (HRV). Starting from recorded ECG data of the patient, for example, as a long-term sequence of the changing RR intervals, the heart rate variability is examined using autocorrelation calculations for the occurrence of rhythmic oscillations of various frequencies. If oscillations typical for apnea occur having very long period durations in the range of 20 to 80 seconds, these are detected as a maximum of the autocorrelation function. If a pathological sleep apnea accordingly exists, individual apnea events may be identified by prompt analysis of short recorded RR sequences, e.g., in the minute interval.
1. A method for detecting sleep apnea from ECG data comprising:
detecting heartbeats as a sequence of R waves using electrodes;
using a data processing component or application-specific integrated circuit or microprocessor in
recording variations of RR interval lengths occurring in said sequence of R waves as a heart rate variability in a heart rate variability log;
analyzing said heart rate variability log for existing heart rate variability oscillations in a low-frequency range using an autocorrelation function
C
(
τ
)
=
∑
i
=
0
N
-
τ
RR
(
i
)
RR
(
i
+
τ
)
wherein N is a number of measured points, RR(i) is a standardized RR interval measured at index i, and τ is a time shift to shift RR in relation to itself at index i+τ, wherein τ passes through a range of integers from τ=0 up to a predefined upper limit;
evaluating said heart rate variability oscillations for existing sleep apnea in a time range between 20 and 120 beats corresponding to said index i by
searching for a first minimum after τ=3 and
searching for a position of an absolute maximum above said first minimum.
2. The method according to claim 1 , further comprising detecting frequency position and/or intensity of oscillations using said autocorrelation function to evaluate a severity of said sleep apnea.
3. The method according to claim 1 , wherein said analyzing said heart rate variability log is restricted to analyzing data ranges of nocturnal and/or sleep phases.
4. The method according to claim 1 , further comprising analyzing a quantity of scattering of a recorded heart rate variability to identify said sleep apnea and a severity of said sleep apnea.
5. The method according to claim 1 , further comprising detecting individual apnea events by analyzing predefined time sections of said heart rate variability log for low frequency oscillations, using absolute value summation of interval differences
S=Σ i=2 N |RR ( i )− RR ( i− 1)|
wherein N is a number of intervals and wherein a current apnea is determined if summation value S is above a predefined threshold.
6. A apparatus for detecting sleep apnea from ECG data comprising:
electrodes configured to detect heartbeats as a sequence of R waves;
a data processing component or application-specific integrated circuit or microprocessor configured to:
record variations of RR interval lengths that occur in said sequence of R waves as a heart rate variability in a heart rate variability log;
analyze said heart rate variability log for existing heart rate variability oscillations in a low-frequency range through use of an autocorrelation function
C
(
τ
)
=
∑
i
=
0
N
-
τ
RR
(
i
)
RR
(
i
+
τ
)
wherein N is a number of measured points, RR(i) is a standardized RR interval measured at index i, and τ is a time shift to shift RR in relation to itself at index i+τ, wherein τ passes through a range of integers from τ=0 up to a predefined upper limit;
evaluate said heart rate variability oscillations for existing sleep apnea in a time range between 20 and 120 beats that corresponds to said index i through
a search for a first minimum after τ=3 and
a search for a position of an absolute maximum above said first minimum.
7. The method according to claim 6 , wherein said data processing component or application-specific integrated circuit or microprocessor is further configured to detect frequency position and/or intensity of oscillations using said autocorrelation to evaluate a severity of said sleep apnea.
8. The method according to claim 6 , wherein said data processing component or application-specific integrated circuit or microprocessor that is configured to analyze said heart rate variability log is restricted to analyze data ranges of nocturnal and/or sleep phases.
9. The method according to claim 6 , wherein said data processing component or application-specific integrated circuit or microprocessor is further configured to analyze a quantity of scattering of a recorded heart rate variability to identify said sleep apnea and a severity of said sleep apnea.
10. The method according to claim 6 , wherein said data processing component or application-specific integrated circuit or microprocessor is further configured to detect individual apnea events through analysis of predefined time sections of said heart rate variability log for low frequency oscillations, through absolute value summation of interval differences
S=Σ i=2 N |RR ( i )− RR ( i− 1)|
wherein N is a number of intervals and wherein a current apnea is determined if summation value S is above a predefined threshold.