Sample　entropy,　a　complexity　measure　that　quantifies　the　new　pattern　generation　rate　of　time　series,　has　been　widely　applied　to　physiological　signal　analysis.　It　can　effectively　reflect　the　pattern　complexity　of　one-dimensional　sequences,　such　as　the　information　contained　in　amplitude　or　period　features.　However,　the　traditional　method　usually　ignores　the　interaction　between　amplitude　and　period　in　time　series,　such　as　electroencephalogram　(EEG)　signals.　To　address　this　issue,　in　this　study,　we　propose　a　new　method　to　describe　the　pattern　complexity　of　waveform　in　a　two-dimensional　space.　In　this　method,　the　local　peaks　of　the　signals　are　first　extracted,　and　the　variation　range　and　the　duration　time　between　the　adjacent　peaks　are　calculated　as　the　instantaneous　amplitude　and　period.　Then　the　amplitude　and　period　sequences　are　combined　into　a　two-dimensional　sequence　to　calculate　the　sample　entropy　based　on　the　amplitude　and　period　information.　In　addition,　in　order　to　avoid　the　influence　of　the　different　units　in　the　two　dimensions,　we　use　the　Jaccard　distance　to　measure　the　similarity　of　the　amplitude-period　bi-vectors　in　the　waveforms,　which　is　different　from　the　one-dimensional　method.　The　Jaccard　distance　is　defined　as　the　ratio　of　the　different　area　to　the　combined　area　of　two　rectangles　containing　the　amplitude-period　bi-vectors　in　the　Cartesian　coordinate　system.　To　verify　the　effectiveness　of　the　method,　we　construct　five　sets　of　simulative　waveforms　in　which　the　numbers　of　patterns　are　completely　equal　in　one-dimensional　space　of　amplitude　or　period　but　the　numbers　in　two-dimensional　space　are　significantly　different　(P＜0.00001).　Simulation　results　show　that　the　two-dimensional　sample　entropy　could　effectively　reflect　the　different　complexities　of　the　five　signals　(P＜0.00001),　while　the　sample　entropy　in　one-dimensional　space　of　amplitude　or　period　cannot　do.　The　results　indicate　that　compared　with　the　one-dimensional　sample　entropy,　the　two-dimensional　sample　entropy　is　very　effective　to　describe　and　distinguish　the　complexity　of　interactive　patterns　based　on　amplitude　and　period　features　in　waveforms.　The　entropy　is　also　used　to　analyze　the　resting　state　EEG　signals　between　well-matched　depression　patient　and　healthy　control　groups.　Signals　in　three　separated　frequency　bands　(Theta,　Alpha,　Beta)　and　ten　brain　regions　(bilateral:　frontal,　central,　parietal,　temporal,　occipital)　are　analyzed.　Experimental　results　show　that　in　the　Alpha　band　and　in　the　left　parietal　and　occipital　regions,　the　two-dimensional　sample　entropy　in　depression　is　significantly　lower　than　that　in　the　healthy　group　(P＜0.01),　indicating　the　disability　of　depression　patients　in　generation　of　various　EEG　patterns.　These　features　might　become　potential　biomarkers　of　depressions.